
""" Importing necessary libraries """
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
import scipy.stats as st
import scipy

from matplotlib.cbook import boxplot_stats as bps


""" Cleaning the ../data/img/ folder """
import os
import glob

files = glob.glob('../data/img/*')
for f in files:
    if os.path.isfile(f) and f.endswith('.png'):
        os.remove(f)

files = glob.glob('../data/img/box-plot/*')
for f in files:
    if os.path.isfile(f) and f.endswith('.png'):
        os.remove(f)


""" Reading the dataset """
disp_df = pd.read_csv("../data/disp_st2ns1.txt.bz2",
                      compression="bz2", index_col=0)
comp_df = pd.read_csv("../data/comp_st2ns1.txt.bz2",
                      compression="bz2", index_col=0)


# Display all the columns of the dataframe
pd.set_option('display.max_columns', None)

disp_df.describe()


disp_df.shape

(4380, 76)


disp_df.head()


disp_df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 4380 entries, V1 to V4380
Data columns (total 76 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   apcp_sf1_1  4380 non-null   float64
 1   apcp_sf2_1  4380 non-null   float64
 2   apcp_sf3_1  4380 non-null   float64
 3   apcp_sf4_1  4380 non-null   float64
 4   apcp_sf5_1  4380 non-null   float64
 5   dlwrf_s1_1  4380 non-null   float64
 6   dlwrf_s2_1  4380 non-null   float64
 7   dlwrf_s3_1  4380 non-null   float64
 8   dlwrf_s4_1  4380 non-null   float64
 9   dlwrf_s5_1  4380 non-null   float64
 10  dswrf_s1_1  4380 non-null   float64
 11  dswrf_s2_1  4380 non-null   float64
 12  dswrf_s3_1  4380 non-null   float64
 13  dswrf_s4_1  4380 non-null   float64
 14  dswrf_s5_1  4380 non-null   float64
 15  pres_ms1_1  4380 non-null   float64
 16  pres_ms2_1  4380 non-null   float64
 17  pres_ms3_1  4380 non-null   float64
 18  pres_ms4_1  4380 non-null   float64
 19  pres_ms5_1  4380 non-null   float64
 20  pwat_ea1_1  4380 non-null   float64
 21  pwat_ea2_1  4380 non-null   float64
 22  pwat_ea3_1  4380 non-null   float64
 23  pwat_ea4_1  4380 non-null   float64
 24  pwat_ea5_1  4380 non-null   float64
 25  spfh_2m1_1  4380 non-null   float64
 26  spfh_2m2_1  4380 non-null   float64
 27  spfh_2m3_1  4380 non-null   float64
 28  spfh_2m4_1  4380 non-null   float64
 29  spfh_2m5_1  4380 non-null   float64
 30  tcdc_ea1_1  4380 non-null   float64
 31  tcdc_ea2_1  4380 non-null   float64
 32  tcdc_ea3_1  4380 non-null   float64
 33  tcdc_ea4_1  4380 non-null   float64
 34  tcdc_ea5_1  4380 non-null   float64
 35  tcolc_e1_1  4380 non-null   float64
 36  tcolc_e2_1  4380 non-null   float64
 37  tcolc_e3_1  4380 non-null   float64
 38  tcolc_e4_1  4380 non-null   float64
 39  tcolc_e5_1  4380 non-null   float64
 40  tmax_2m1_1  4380 non-null   float64
 41  tmax_2m2_1  4380 non-null   float64
 42  tmax_2m3_1  4380 non-null   float64
 43  tmax_2m4_1  4380 non-null   float64
 44  tmax_2m5_1  4380 non-null   float64
 45  tmin_2m1_1  4380 non-null   float64
 46  tmin_2m2_1  4380 non-null   float64
 47  tmin_2m3_1  4380 non-null   float64
 48  tmin_2m4_1  4380 non-null   float64
 49  tmin_2m5_1  4380 non-null   float64
 50  tmp_2m_1_1  4380 non-null   float64
 51  tmp_2m_2_1  4380 non-null   float64
 52  tmp_2m_3_1  4380 non-null   float64
 53  tmp_2m_4_1  4380 non-null   float64
 54  tmp_2m_5_1  4380 non-null   float64
 55  tmp_sfc1_1  4380 non-null   float64
 56  tmp_sfc2_1  4380 non-null   float64
 57  tmp_sfc3_1  4380 non-null   float64
 58  tmp_sfc4_1  4380 non-null   float64
 59  tmp_sfc5_1  4380 non-null   float64
 60  ulwrf_s1_1  4380 non-null   float64
 61  ulwrf_s2_1  4380 non-null   float64
 62  ulwrf_s3_1  4380 non-null   float64
 63  ulwrf_s4_1  4380 non-null   float64
 64  ulwrf_s5_1  4380 non-null   float64
 65  ulwrf_t1_1  4380 non-null   float64
 66  ulwrf_t2_1  4380 non-null   float64
 67  ulwrf_t3_1  4380 non-null   float64
 68  ulwrf_t4_1  4380 non-null   float64
 69  ulwrf_t5_1  4380 non-null   float64
 70  uswrf_s1_1  4380 non-null   float64
 71  uswrf_s2_1  4380 non-null   float64
 72  uswrf_s3_1  4380 non-null   float64
 73  uswrf_s4_1  4380 non-null   float64
 74  uswrf_s5_1  4380 non-null   float64
 75  salida      4380 non-null   int64  
dtypes: float64(75), int64(1)
memory usage: 2.6+ MB


disp_df.isna().sum()

apcp_sf1_1    0
apcp_sf2_1    0
apcp_sf3_1    0
apcp_sf4_1    0
apcp_sf5_1    0
             ..
uswrf_s2_1    0
uswrf_s3_1    0
uswrf_s4_1    0
uswrf_s5_1    0
salida        0
Length: 76, dtype: int64


# In the plot, we can see that there are a lot of 0 values in the dataset
disp_df.plot(legend=False, figsize=(15, 5))

<AxesSubplot:>


result = disp_df.eq(0.0).sum()/len(disp_df)*100

# Select those columns with more than 30% of zeros
result = result[result > 30.0]
result = result.sort_values(ascending=False)
result

dswrf_s1_1    92.054795
uswrf_s1_1    90.844749
apcp_sf4_1    63.378995
apcp_sf5_1    61.278539
apcp_sf1_1    61.164384
apcp_sf2_1    60.296804
apcp_sf3_1    57.305936
tcdc_ea3_1    37.945205
tcdc_ea1_1    37.237443
tcdc_ea2_1    37.123288
tcdc_ea5_1    36.004566
tcdc_ea4_1    35.547945
dtype: float64


disp_df_nan = disp_df.replace(0.0, np.nan)


""" Plotting missing values """
# Sustitute 0.0 values with NaN and plot the name of the columns with missing values
# ? msno.bar is a simple visualization of nullity by column
msno.bar(disp_df_nan, labels=True, fontsize=7, figsize=(15, 7))

# Exporting image as png to ../data/img folder
plt.savefig("../data/img/missing_values_bar.png")


""" Plotting the missing values in a matrix """
# ? The msno.matrix nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
msno.matrix(disp_df_nan, labels=True, fontsize=7, figsize=(15, 7), color=(.3, .3, .3))

# Exporting image as png to ../data/img folder
plt.savefig("../data/img/missing_values_matrix.png")


""" Plotting the missing values in a heatmap """
# As in a hetmap not every value is shown, we must delimit the values to the ones with more than 30% of missing values
result = disp_df.eq(0.0).sum()/len(disp_df)*100
result = result[result > 30.0] # Select those columns with more than 30% of zeros
result = result.sort_values(ascending=False)
result = result.index.tolist() # Convert to list
result

# ? The missingno correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another
msno.heatmap(disp_df_nan[result], fontsize=7, figsize=(15, 7))

# Exporting image as png to ../data/img folder
plt.savefig("../data/img/missing_values_heatmap.png")


""" Plotting the dendrogram """

# ? The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap:
msno.dendrogram(disp_df_nan, orientation="top",fontsize=7, figsize=(15, 7))

# Exporting image as png to ../data/img folder
plt.savefig("../data/img/missing_values_dendrogram.png")


list_of_attributes = disp_df.columns.values.tolist()
#print(list_of_attributes)


# Boxplot with all attributes in the dataset
# sns.boxplot(data=disp_df, orient="h")
# plt.show()


disp_df.describe()


## disp_df['apcp_sf1_1'].value_counts()


""" Plotting the boxplot for each attribute and getting the outliers of each attribute """
total_outliers = []
# * We iterate over the list of attributes
for attribute in list_of_attributes:
    # * sns.regplot(x=disp_df[attribute], y=disp_df['total'], fit_reg=False)
    sns.boxplot(data=disp_df[attribute], x=disp_df[attribute], orient="h")
    # * Use the command below to show each plot (small size for visualization sake)
    # sns.set(rc={'figure.figsize':(1,.5)})
    # plt.show()
    # * All the images are saved in the folder ../data/img/box-plot
    plt.savefig(f"../data/img/box-plot/{str(attribute)}.png")

    # We obtain the a list of outliers for each attribute
    list_of_outliers = disp_df[attribute][disp_df[attribute] > disp_df[attribute].quantile(0.75) + 1.5*(disp_df[attribute].quantile(0.75) - disp_df[attribute].quantile(0.25))].tolist()
    outliers = [f'{attribute} outliers'] + [len(list_of_outliers)] + [list_of_outliers]
    # * In orde to print the total number of outliers for each attribute
    # print(f'{attribute} has {len(list_of_outliers)} outliers')
    # ! Data structure: [attribute, number of outliers, list of outliers]
    print(outliers)
    total_outliers.append(outliers)

print(total_outliers)

['apcp_sf1_1 outliers', 839, [1.11909090930765, 0.358181819997051, 2.46363633329218, 0.510000003332442, 1.12363637577404, 26.886363549666, 0.942727267742157, 1.08727271990343, 11.872727134011, 0.358181823383678, 0.2845454569906, 4.84181818095121, 6.9500000260093, 5.18909092382951, 7.09727272120389, 0.916363634846427, 0.448181818832051, 3.9127273153175, 3.16363642432473, 1.2427272695032, 2.06454543633894, 6.27000004188581, 1.0327272862196, 0.31909091161056, 2.17272726785053, 0.876363634724509, 2.17636364630678, 0.487272722815925, 1.37545453960245, 1.01727271080017, 0.504545454274524, 1.41818179122426, 8.4618182182312, 1.12727272510529, 4.05181815881621, 0.94454545324499, 4.33272725885565, 0.670000000433488, 0.358181819997051, 0.418181818994609, 10.9018183621493, 0.512727274474773, 3.700909094377, 5.45909088308161, 0.351818186992949, 1.35636362907561, 0.296363632787358, 0.409090909768235, 6.56000002947721, 6.44272729483518, 2.39000003039837, 1.08272727240216, 3.78181821649725, 0.45545454594222, 3.58909089998765, 3.91545449603688, 0.572727272456342, 1.75636361945759, 1.90000000866977, 0.463636354966597, 4.7136363549666, 1.68636362119154, 6.22545459053733, 3.92181821302934, 1.46545452421362, 28.6190910339355, 0.390909087928859, 0.367272729223425, 0.639090915972536, 0.313636367971247, 2.27272728356448, 2.30454544587569, 0.525454550981522, 4.22181820869446, 7.90272734381936, 0.300909101963043, 0.330909093672579, 0.504545450210571, 0.292727279392156, 1.77090911973606, 0.79818180474368, 0.424545458771966, 2.80545456842943, 0.641818176616322, 17.1690908778797, 1.97272727706216, 0.539090902290561, 2.97454549914057, 0.378181820566004, 2.07272729548541, 1.23545454848896, 7.32181820002469, 0.565454542636871, 0.80727272819389, 2.13000000606884, 0.357272728600285, 0.770909078249877, 0.779090911149979, 6.95636356960643, 0.65727273137732, 18.6727274114435, 0.543636381287466, 0.83545454997908, 0.76272727413611, 2.72636364180256, 2.11636363918131, 0.717272729020227, 5.15181822126562, 0.322727283293551, 10.397272716869, 0.41454545611685, 2.42818182554435, 0.502727262675762, 2.5536363883452, 2.6454545584592, 0.37454545091499, 1.98636362769387, 1.0618181926283, 19.4163636727767, 13.7645454840227, 1.44727270440622, 1.67000000585209, 4.53909091515975, 6.80181812833656, 3.49999995123256, 1.20636362108317, 20.0018183101307, 3.71818180517717, 0.645454550331289, 0.434545450081879, 1.57636363397945, 1.32272725755518, 5.3500000780279, 0.878181817856702, 1.9018181995912, 0.44545455141501, 1.09636363658038, 0.473636364394968, 0.384545461698012, 0.370909094810486, 1.48909089782021, 0.391818182034926, 0.429999999024651, 1.70454546538266, 12.6972728209062, 1.13818182254379, 1.67727274244482, 6.99363639137962, 0.372727276249365, 3.55636366930875, 0.534545455974611, 2.29363635961305, 0.414545457471501, 3.86363630945032, 0.530909104611386, 9.36272725191983, 0.84272725998678, 0.483636368404735, 1.40090907161886, 1.24818180907856, 2.66545453938571, 10.9272726232355, 9.63181829452515, 1.5727272819389, 2.32999999668788, 0.347272736613046, 2.20727270164273, 1.33363635431636, 0.427272729236971, 0.869999988512559, 2.52545454285362, 0.816363638774915, 1.82909093255346, 0.343636370517991, 0.34545454450629, 1.64818180149252, 0.367272731932727, 19.2481820366599, 4.26909083669836, 1.68454544110732, 0.702727283943783, 27.2272729006681, 0.445454555478963, 0.414545446464961, 3.20454548705708, 2.01909091255882, 0.551818183877251, 4.35909090258858, 0.383636361834678, 2.20909091559323, 2.44818179173903, 1.28181817585772, 1.27272725647146, 0.877272725105286, 1.83545451001687, 0.32363636656241, 1.26000000942837, 0.372727277604016, 0.350000003522093, 1.98545454307036, 13.6281818910079, 5.66999996792186, 0.280909093943509, 0.374545455656268, 0.276363636959683, 0.467272728681564, 1.52909089760347, 1.22272726080634, 0.873636367646131, 1.12090907313607, 1.17272727597844, 15.347272829576, 1.45272728529843, 3.53181817314842, 0.305454548109661, 1.92454544522546, 2.6090909296816, 0.899999991736629, 0.311818187887018, 0.554545464840802, 6.33545455065641, 0.572727272456342, 3.12363638661125, 5.0236363844438, 0.404545453461734, 15.4318182685158, 0.654545466330918, 7.28454550829801, 12.6254546425559, 2.94909093596719, 0.986363633112474, 4.02727274461226, 9.77363640611822, 8.0363635149869, 5.95454542203383, 0.672727274623784, 4.68272729353471, 1.99272729050029, 3.63181815906004, 0.750909109007229, 18.9118181575428, 0.410000006583604, 1.50272727283564, 0.686363633383404, 4.88363636352799, 0.390909096056765, 1.22000000558116, 8.08181806044145, 0.770909079096534, 2.35636362162503, 0.920000000433488, 5.42272721637379, 1.27000001343814, 3.19636364281178, 5.18181818181818, 9.32363627173684, 1.88000002232465, 3.06818181818182, 0.430000000379302, 5.72545456886292, 0.722727279771458, 8.76636370745572, 2.24181819910353, 9.17636368491433, 1.549090905623, 6.32999996705489, 1.96727270429785, 0.420909092507579, 2.23727270689878, 13.8454544760964, 11.0963635878129, 9.75454551523382, 5.13999999653209, 1.89090909470211, 0.548181827603416, 0.283636360005899, 7.75454558025707, 0.619090906598351, 2.11999998648058, 0.591818186369809, 1.29000000520186, 1.19999999078837, 2.80909091776068, 1.45545453375036, 3.46272729743611, 4.33636366237294, 0.770000008019534, 0.350000003522093, 0.513636361468922, 13.8272727619518, 2.59909090128812, 1.02727273377505, 0.500000005418604, 24.881818077781, 3.58181819048795, 5.19090906056491, 2.42636363072829, 4.42727264490995, 0.278181826526468, 4.77000004594976, 1.38272729245099, 2.00363634933125, 0.327272732826796, 0.318181824277748, 0.282727275890383, 0.537272729656913, 1.03454543514685, 4.12545453418385, 1.02999999577349, 0.317272732203657, 0.930000007152557, 0.685454539277337, 0.293636370788921, 1.77181817184795, 3.00545454025269, 2.30454544587569, 5.54818175055764, 0.396363645792007, 0.497272731228308, 0.754545458338477, 1.2554545592178, 1.49090910499746, 1.22909091633152, 4.54818181558089, 9.58818190748041, 2.29181817444888, 3.50272731347518, 6.21818191083995, 10.130909096111, 2.95272726904262, 8.49818181991577, 6.55181815407493, 2.35999997976151, 1.84636362574317, 0.428181829100305, 0.720909096300602, 0.370909090238539, 3.09090905924412, 0.997272753241387, 3.09909093379974, 2.62636362422596, 0.309090915051374, 0.919999998401512, 8.36181820522655, 0.370000002228401, 0.358181825754317, 0.349090909924019, 0.441818183795972, 1.55999999967488, 1.13818181373856, 0.319090915674513, 15.5790910287337, 0.515454542907802, 2.2918181961233, 0.487272727218541, 0.628181820566004, 2.44181817770004, 6.47181814367121, 6.49818179824136, 10.4854546460238, 3.71818185936321, 0.715454563498497, 2.00000001219186, 5.19363635507497, 2.68545454334129, 0.555454549464312, 0.345454549247568, 0.362727272747592, 0.434545451944525, 1.51272724433379, 2.77636367082596, 0.798181812871586, 2.87818182869391, 0.957272730090401, 1.8890909281644, 0.647272724996914, 0.492727282372388, 0.795454548163848, 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1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.181818181818182, 1.0]], ['uswrf_s2_1 outliers', 0, []], ['uswrf_s3_1 outliers', 3, [190.545454545455, 192.636363636364, 184.636363636364]], ['uswrf_s4_1 outliers', 35, [238.363636363636, 247.272727272727, 227.636363636364, 246.454545454545, 322.727272727273, 382.181818181818, 334.090909090909, 325.818181818182, 256.181818181818, 241.0, 260.545454545455, 306.363636363636, 240.363636363636, 292.272727272727, 229.0, 281.454545454545, 227.545454545455, 239.818181818182, 304.272727272727, 327.818181818182, 450.636363636364, 377.909090909091, 343.090909090909, 278.909090909091, 236.454545454545, 246.545454545455, 319.818181818182, 289.090909090909, 324.636363636364, 263.636363636364, 250.909090909091, 292.727272727273, 274.909090909091, 244.545454545455, 255.727272727273]], ['uswrf_s5_1 outliers', 9, [229.272727272727, 270.272727272727, 218.818181818182, 220.363636363636, 221.090909090909, 313.909090909091, 242.090909090909, 223.090909090909, 239.727272727273]], ['salida outliers', 0, []]]


""" Skewness """
# ? skewness: measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
disp_df.skew().sort_values(ascending=False)

apcp_sf4_1    9.232876
apcp_sf2_1    7.550050
apcp_sf1_1    7.402803
apcp_sf3_1    7.265894
apcp_sf5_1    7.195886
                ...   
ulwrf_t1_1   -0.965765
ulwrf_t3_1   -0.997749
ulwrf_t2_1   -1.007615
ulwrf_t5_1   -1.055472
ulwrf_t4_1   -1.181864
Length: 76, dtype: float64


""" Kurtosis """
# ? kurtosis: measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution.
disp_df.kurt().sort_values(ascending=False)

apcp_sf4_1    137.078212
apcp_sf1_1     78.306178
apcp_sf2_1     76.959166
apcp_sf5_1     76.901878
apcp_sf3_1     70.852519
                 ...    
uswrf_s2_1     -1.303016
spfh_2m2_1     -1.318701
spfh_2m5_1     -1.319063
dswrf_s2_1     -1.321058
spfh_2m3_1     -1.324692
Length: 76, dtype: float64


y = disp_df['apcp_sf4_1']
plt.figure(1); plt.title('Normal')
sns.distplot(y, kde=True, fit=st.norm)
plt.figure(2); plt.title('Log Normal')
sns.distplot(y, kde=True, fit=st.lognorm)

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:title={'center':'Log Normal'}, xlabel='apcp_sf4_1', ylabel='Density'>


sns.distplot(disp_df.skew(),color='blue',axlabel ='Skewness')

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='Skewness', ylabel='Density'>


plt.figure(figsize = (12,8))
sns.distplot(disp_df.kurt(),color='r',axlabel ='Kurtosis',norm_hist= False, kde = True,rug = False)
#plt.hist(train.kurt(),orientation = 'vertical',histtype = 'bar',label ='Kurtosis', color ='blue')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


correlation = disp_df.corr()
correlation = abs(correlation)
print(correlation)  # 76 x 76 matrix of correlation values

            apcp_sf1_1  apcp_sf2_1  apcp_sf3_1  apcp_sf4_1  apcp_sf5_1  \
apcp_sf1_1    1.000000    0.587239    0.509665    0.249329    0.255535   
apcp_sf2_1    0.587239    1.000000    0.926354    0.394384    0.379884   
apcp_sf3_1    0.509665    0.926354    1.000000    0.574310    0.533648   
apcp_sf4_1    0.249329    0.394384    0.574310    1.000000    0.928523   
apcp_sf5_1    0.255535    0.379884    0.533648    0.928523    1.000000   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.161360    0.204639    0.212301    0.135941    0.148588   
uswrf_s3_1    0.228271    0.313188    0.343573    0.252657    0.272716   
uswrf_s4_1    0.181360    0.279789    0.340832    0.350630    0.371776   
uswrf_s5_1    0.145594    0.235880    0.291234    0.309841    0.335141   
salida        0.171532    0.248745    0.282336    0.249886    0.275859   

            dlwrf_s1_1  dlwrf_s2_1  dlwrf_s3_1  dlwrf_s4_1  dlwrf_s5_1  \
apcp_sf1_1    0.252467    0.229487    0.199415    0.117486    0.106691   
apcp_sf2_1    0.208902    0.221926    0.197477    0.121631    0.108390   
apcp_sf3_1    0.211564    0.228722    0.206805    0.132780    0.119427   
apcp_sf4_1    0.180722    0.200900    0.191789    0.155093    0.147178   
apcp_sf5_1    0.179887    0.205593    0.197236    0.163057    0.156918   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.432580    0.406560    0.454606    0.562219    0.582468   
uswrf_s3_1    0.210601    0.161007    0.198846    0.315337    0.341813   
uswrf_s4_1    0.035897    0.013393    0.001412    0.042395    0.065821   
uswrf_s5_1    0.209446    0.169964    0.187348    0.231418    0.249300   
salida        0.254926    0.227098    0.263239    0.356157    0.377296   

            dswrf_s1_1  dswrf_s2_1  dswrf_s3_1  dswrf_s4_1  dswrf_s5_1  \
apcp_sf1_1    0.001964    0.128776    0.191791    0.168884    0.129189   
apcp_sf2_1    0.020380    0.174249    0.275433    0.270171    0.218423   
apcp_sf3_1    0.022460    0.180577    0.303273    0.328937    0.269387   
apcp_sf4_1    0.011465    0.105525    0.209245    0.312108    0.264529   
apcp_sf5_1    0.018218    0.115553    0.225397    0.328205    0.283284   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.303526    0.991282    0.959154    0.824318    0.861714   
uswrf_s3_1    0.234205    0.863920    0.943955    0.878819    0.864104   
uswrf_s4_1    0.159717    0.575545    0.718675    0.847756    0.798589   
uswrf_s5_1    0.218498    0.727424    0.823901    0.924133    0.920314   
salida        0.240142    0.809592    0.863117    0.848158    0.859374   

            pres_ms1_1  pres_ms2_1  pres_ms3_1  pres_ms4_1  pres_ms5_1  \
apcp_sf1_1    0.146848    0.148820    0.136175    0.119454    0.104211   
apcp_sf2_1    0.111433    0.119972    0.114768    0.103043    0.093229   
apcp_sf3_1    0.108703    0.120576    0.114731    0.103314    0.095204   
apcp_sf4_1    0.093839    0.104674    0.107489    0.095110    0.090786   
apcp_sf5_1    0.099140    0.111459    0.116843    0.108712    0.103477   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.252792    0.253788    0.256780    0.250845    0.309394   
uswrf_s3_1    0.106753    0.099597    0.104956    0.103336    0.154104   
uswrf_s4_1    0.013838    0.030146    0.030660    0.026535    0.005369   
uswrf_s5_1    0.096664    0.083119    0.079680    0.080814    0.124234   
salida        0.153849    0.148971    0.155926    0.161058    0.217138   

            pwat_ea1_1  pwat_ea2_1  pwat_ea3_1  pwat_ea4_1  pwat_ea5_1  \
apcp_sf1_1    0.296274    0.244169    0.198330    0.170203    0.149204   
apcp_sf2_1    0.277081    0.267178    0.224550    0.187166    0.159046   
apcp_sf3_1    0.279227    0.280660    0.249724    0.211207    0.179108   
apcp_sf4_1    0.232377    0.251122    0.263689    0.255625    0.218597   
apcp_sf5_1    0.228993    0.249242    0.264037    0.265891    0.242646   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.384125    0.402023    0.441876    0.473342    0.484551   
uswrf_s3_1    0.179009    0.184738    0.219945    0.255811    0.273678   
uswrf_s4_1    0.023363    0.006873    0.008959    0.023912    0.039599   
uswrf_s5_1    0.184827    0.171067    0.174248    0.183744    0.192119   
salida        0.199647    0.200626    0.225119    0.252539    0.268058   

            spfh_2m1_1  spfh_2m2_1  spfh_2m3_1  spfh_2m4_1  spfh_2m5_1  \
apcp_sf1_1    0.198389    0.144806    0.124807    0.116133    0.091427   
apcp_sf2_1    0.152095    0.115717    0.105344    0.098780    0.067427   
apcp_sf3_1    0.154382    0.118323    0.109713    0.102646    0.066615   
apcp_sf4_1    0.146267    0.122468    0.125389    0.128654    0.089437   
apcp_sf5_1    0.144502    0.119714    0.125393    0.132499    0.097667   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.504600    0.609473    0.629637    0.635755    0.679233   
uswrf_s3_1    0.307413    0.407393    0.426305    0.429623    0.476343   
uswrf_s4_1    0.124084    0.194859    0.201843    0.194814    0.236298   
uswrf_s5_1    0.292288    0.369628    0.374909    0.366379    0.412005   
salida        0.343532    0.435309    0.445177    0.443821    0.497415   

            tcdc_ea1_1  tcdc_ea2_1  tcdc_ea3_1  tcdc_ea4_1  tcdc_ea5_1  \
apcp_sf1_1    0.596741    0.398746    0.265764    0.198492    0.160341   
apcp_sf2_1    0.672834    0.715149    0.502922    0.350019    0.261632   
apcp_sf3_1    0.637130    0.796274    0.668624    0.471125    0.338706   
apcp_sf4_1    0.352903    0.478388    0.642517    0.679871    0.473290   
apcp_sf5_1    0.358603    0.473176    0.613378    0.723144    0.616474   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.368145    0.362683    0.301227    0.266248    0.222930   
uswrf_s3_1    0.473477    0.536721    0.490033    0.417147    0.346792   
uswrf_s4_1    0.382498    0.493303    0.594999    0.582924    0.457324   
uswrf_s5_1    0.341099    0.432123    0.523418    0.556448    0.463889   
salida        0.376972    0.416313    0.416811    0.413036    0.363354   

            tcolc_e1_1  tcolc_e2_1  tcolc_e3_1  tcolc_e4_1  tcolc_e5_1  \
apcp_sf1_1    0.596999    0.398765    0.265694    0.198500    0.160295   
apcp_sf2_1    0.673016    0.715288    0.502904    0.349953    0.261611   
apcp_sf3_1    0.637236    0.796417    0.668628    0.471031    0.338683   
apcp_sf4_1    0.352844    0.478265    0.642587    0.679941    0.473425   
apcp_sf5_1    0.358470    0.473050    0.613428    0.723196    0.616690   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.367846    0.362396    0.300915    0.266016    0.222316   
uswrf_s3_1    0.473116    0.536377    0.489661    0.416919    0.346277   
uswrf_s4_1    0.382194    0.493022    0.594665    0.582660    0.456995   
uswrf_s5_1    0.340784    0.431847    0.523049    0.556127    0.463424   
salida        0.376667    0.415967    0.416381    0.412663    0.362822   

            tmax_2m1_1  tmax_2m2_1  tmax_2m3_1  tmax_2m4_1  tmax_2m5_1  \
apcp_sf1_1    0.086320    0.047192    0.001262    0.013349    0.011220   
apcp_sf2_1    0.055737    0.021067    0.033114    0.048487    0.046075   
apcp_sf3_1    0.051982    0.019412    0.044697    0.065578    0.063010   
apcp_sf4_1    0.067483    0.046021    0.007090    0.035950    0.034657   
apcp_sf5_1    0.061450    0.039397    0.014232    0.041445    0.040312   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.617625    0.707818    0.729943    0.727643    0.726156   
uswrf_s3_1    0.422566    0.510680    0.566898    0.573640    0.570316   
uswrf_s4_1    0.189920    0.250626    0.316016    0.341662    0.337773   
uswrf_s5_1    0.362295    0.428291    0.480080    0.503243    0.501006   
salida        0.487250    0.563333    0.617623    0.633928    0.632514   

            tmin_2m1_1  tmin_2m2_1  tmin_2m3_1  tmin_2m4_1  tmin_2m5_1  \
apcp_sf1_1    0.100939    0.100460    0.095766    0.012211    0.002927   
apcp_sf2_1    0.075889    0.077571    0.073516    0.046884    0.029493   
apcp_sf3_1    0.074597    0.077362    0.073828    0.061058    0.043028   
apcp_sf4_1    0.084952    0.088374    0.087045    0.026905    0.017712   
apcp_sf5_1    0.080793    0.084296    0.083079    0.032528    0.023297   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.590413    0.609540    0.611692    0.734319    0.748741   
uswrf_s3_1    0.385303    0.398022    0.401007    0.576777    0.575421   
uswrf_s4_1    0.152155    0.158895    0.161367    0.333741    0.330719   
uswrf_s5_1    0.327046    0.338194    0.340349    0.494949    0.508875   
salida        0.454882    0.466277    0.469009    0.630224    0.636792   

            tmp_2m_1_1  tmp_2m_2_1  tmp_2m_3_1  tmp_2m_4_1  tmp_2m_5_1  \
apcp_sf1_1    0.103823    0.043312    0.006389    0.019174    0.009648   
apcp_sf2_1    0.079303    0.017578    0.040359    0.054972    0.021280   
apcp_sf3_1    0.078411    0.016176    0.052030    0.073926    0.034905   
apcp_sf4_1    0.087733    0.044698    0.011419    0.051073    0.015577   
apcp_sf5_1    0.083463    0.038199    0.018123    0.056298    0.021263   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.593704    0.710829    0.731880    0.730399    0.743679   
uswrf_s3_1    0.386181    0.514458    0.571316    0.579273    0.564870   
uswrf_s4_1    0.152663    0.252925    0.320777    0.353187    0.321805   
uswrf_s5_1    0.329131    0.430199    0.483446    0.513929    0.504323   
salida        0.455411    0.567001    0.621700    0.642426    0.631714   

            tmp_sfc1_1  tmp_sfc2_1  tmp_sfc3_1  tmp_sfc4_1  tmp_sfc5_1  \
apcp_sf1_1    0.117665    0.028723    0.046670    0.039074    0.020654   
apcp_sf2_1    0.092290    0.000252    0.091224    0.080745    0.009330   
apcp_sf3_1    0.091986    0.000895    0.109692    0.104061    0.020645   
apcp_sf4_1    0.098414    0.034913    0.056371    0.081749    0.000421   
apcp_sf5_1    0.095258    0.028059    0.064955    0.088254    0.004456   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.587679    0.734504    0.735255    0.735435    0.751148   
uswrf_s3_1    0.373165    0.541160    0.604498    0.599004    0.562288   
uswrf_s4_1    0.140571    0.273655    0.368381    0.390610    0.311259   
uswrf_s5_1    0.320457    0.452301    0.520793    0.550483    0.499459   
salida        0.440532    0.588912    0.655115    0.667369    0.624655   

            ulwrf_s1_1  ulwrf_s2_1  ulwrf_s3_1  ulwrf_s4_1  ulwrf_s5_1  \
apcp_sf1_1    0.105608    0.076806    0.028030    0.048602    0.029815   
apcp_sf2_1    0.076194    0.051626    0.004015    0.091834    0.069451   
apcp_sf3_1    0.073620    0.051608    0.008715    0.114263    0.089611   
apcp_sf4_1    0.083396    0.071053    0.026707    0.078799    0.062584   
apcp_sf5_1    0.078631    0.065907    0.019634    0.085594    0.069320   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.603445    0.685863    0.725149    0.737691    0.749799   
uswrf_s3_1    0.403292    0.477852    0.543333    0.611675    0.606966   
uswrf_s4_1    0.176445    0.227641    0.290313    0.402397    0.389095   
uswrf_s5_1    0.351003    0.411266    0.464873    0.554224    0.553687   
salida        0.463277    0.529134    0.589455    0.667654    0.666399   

            ulwrf_t1_1  ulwrf_t2_1  ulwrf_t3_1  ulwrf_t4_1  ulwrf_t5_1  \
apcp_sf1_1    0.409664    0.306309    0.263891    0.153687    0.137968   
apcp_sf2_1    0.358439    0.384647    0.373792    0.263938    0.239939   
apcp_sf3_1    0.355456    0.399221    0.408142    0.338837    0.309519   
apcp_sf4_1    0.223813    0.279267    0.308837    0.361607    0.359904   
apcp_sf5_1    0.234572    0.294328    0.327546    0.381898    0.398008   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.535560    0.610153    0.615371    0.529139    0.536234   
uswrf_s3_1    0.521208    0.606069    0.637370    0.591476    0.583330   
uswrf_s4_1    0.367799    0.454166    0.508113    0.592394    0.583309   
uswrf_s5_1    0.411066    0.499054    0.548504    0.627809    0.643032   
salida        0.511566    0.579722    0.613113    0.612886    0.622833   

            uswrf_s1_1  uswrf_s2_1  uswrf_s3_1  uswrf_s4_1  uswrf_s5_1  \
apcp_sf1_1    0.047574    0.161360    0.228271    0.181360    0.145594   
apcp_sf2_1    0.053898    0.204639    0.313188    0.279789    0.235880   
apcp_sf3_1    0.047203    0.212301    0.343573    0.340832    0.291234   
apcp_sf4_1    0.012111    0.135941    0.252657    0.350630    0.309841   
apcp_sf5_1    0.009908    0.148588    0.272716    0.371776    0.335141   
...                ...         ...         ...         ...         ...   
uswrf_s2_1    0.468159    1.000000    0.905633    0.629272    0.761853   
uswrf_s3_1    0.360956    0.905633    1.000000    0.854613    0.891581   
uswrf_s4_1    0.217326    0.629272    0.854613    1.000000    0.956773   
uswrf_s5_1    0.291304    0.761853    0.891581    0.956773    1.000000   
salida        0.324173    0.830794    0.830235    0.690593    0.794012   

              salida  
apcp_sf1_1  0.171532  
apcp_sf2_1  0.248745  
apcp_sf3_1  0.282336  
apcp_sf4_1  0.249886  
apcp_sf5_1  0.275859  
...              ...  
uswrf_s2_1  0.830794  
uswrf_s3_1  0.830235  
uswrf_s4_1  0.690593  
uswrf_s5_1  0.794012  
salida      1.000000  

[76 rows x 76 columns]


correlation_list = []

for column in disp_df:
    correlation[column] = abs(correlation[column])
    mask = correlation[column] > 0.95
    # print(correlation[column][mask].sort_values(ascending = False))
    
    # Translate the comment below to English: 
    # we add the correlation values to a list of lists, which contains the names of the correlated columns and their correlation index
    
    # The first segment adds the name of the column we are analyzing
    # The second segment adds the names of the columns correlated (except the column we are analyzing) > 0.95
    # The third segment adds the correlation index of the columns correlated (except the column we are analyzing) > 0.95
    # Second and third segment are added to the first segment as a list of lists
    
    # First we need to create a dictionary with the column names and their correlation values (except the column we are analyzing)
    dict = ({key: value for key, value in correlation[column][mask].sort_values(ascending = False).to_dict().items() if key != column})
    # print (dict)
    
    # Then we create a list of lists with the column names and their correlation values from the dictionary created above
    corr_list = [[key] + [value] for key, value in dict.items()]
    # Finally we add the name of the column we are analyzing to the list of lists created above as the first element of the list (str)
    corr_list.insert(0, f"Columna: {column}")
    
    # ! Data structure: [[columna, [columna correlada 1, indice de correlacion], [columna correlada 2, indice de correlacion], ...], ...] 
    print(corr_list)
    
    correlation_list += [corr_list]
print(correlation_list)

['Columna: apcp_sf1_1']
['Columna: apcp_sf2_1']
['Columna: apcp_sf3_1']
['Columna: apcp_sf4_1']
['Columna: apcp_sf5_1']
['Columna: dlwrf_s1_1', ['dlwrf_s2_1', 0.9654602451282467], ['dlwrf_s3_1', 0.9555714275535937]]
['Columna: dlwrf_s2_1', ['dlwrf_s3_1', 0.993801937354884], ['dlwrf_s1_1', 0.9654602451282467]]
['Columna: dlwrf_s3_1', ['dlwrf_s2_1', 0.993801937354884], ['dlwrf_s4_1', 0.9664287191255341], ['dlwrf_s5_1', 0.955638710623432], ['dlwrf_s1_1', 0.9555714275535937]]
['Columna: dlwrf_s4_1', ['dlwrf_s5_1', 0.9968432736329301], ['dlwrf_s3_1', 0.9664287191255341]]
['Columna: dlwrf_s5_1', ['dlwrf_s4_1', 0.9968432736329301], ['dlwrf_s3_1', 0.955638710623432]]
['Columna: dswrf_s1_1']
['Columna: dswrf_s2_1', ['uswrf_s2_1', 0.9912815245133624], ['dswrf_s3_1', 0.9503100565387964]]
['Columna: dswrf_s3_1', ['uswrf_s2_1', 0.9591543590605579], ['dswrf_s2_1', 0.9503100565387964]]
['Columna: dswrf_s4_1', ['dswrf_s5_1', 0.9825798647066863]]
['Columna: dswrf_s5_1', ['dswrf_s4_1', 0.9825798647066863]]
['Columna: pres_ms1_1', ['pres_ms2_1', 0.9880297011546728], ['pres_ms3_1', 0.9578904939482652]]
['Columna: pres_ms2_1', ['pres_ms1_1', 0.9880297011546728], ['pres_ms3_1', 0.9874219399163731], ['pres_ms4_1', 0.9552800186140463]]
['Columna: pres_ms3_1', ['pres_ms2_1', 0.9874219399163731], ['pres_ms4_1', 0.9871476057524855], ['pres_ms1_1', 0.9578904939482652], ['pres_ms5_1', 0.9555376601620624]]
['Columna: pres_ms4_1', ['pres_ms3_1', 0.9871476057524855], ['pres_ms5_1', 0.9857526889691135], ['pres_ms2_1', 0.9552800186140463]]
['Columna: pres_ms5_1', ['pres_ms4_1', 0.9857526889691135], ['pres_ms3_1', 0.9555376601620624]]
['Columna: pwat_ea1_1', ['pwat_ea2_1', 0.9859162230035117], ['pwat_ea3_1', 0.9579571492658394]]
['Columna: pwat_ea2_1', ['pwat_ea3_1', 0.987435297363512], ['pwat_ea1_1', 0.9859162230035117], ['pwat_ea4_1', 0.9616516049128513]]
['Columna: pwat_ea3_1', ['pwat_ea4_1', 0.9880573471002622], ['pwat_ea2_1', 0.987435297363512], ['pwat_ea5_1', 0.9616115768986265], ['pwat_ea1_1', 0.9579571492658394]]
['Columna: pwat_ea4_1', ['pwat_ea3_1', 0.9880573471002622], ['pwat_ea5_1', 0.9867846903534372], ['pwat_ea2_1', 0.9616516049128513]]
['Columna: pwat_ea5_1', ['pwat_ea4_1', 0.9867846903534372], ['pwat_ea3_1', 0.9616115768986265]]
['Columna: spfh_2m1_1', ['spfh_2m2_1', 0.9740287040850961]]
['Columna: spfh_2m2_1', ['spfh_2m3_1', 0.9851324272253641], ['spfh_2m1_1', 0.9740287040850961], ['spfh_2m4_1', 0.9611823078725884]]
['Columna: spfh_2m3_1', ['spfh_2m4_1', 0.9893999492368546], ['spfh_2m2_1', 0.9851324272253641], ['spfh_2m5_1', 0.9776313743577926]]
['Columna: spfh_2m4_1', ['spfh_2m5_1', 0.9904992516406356], ['spfh_2m3_1', 0.9893999492368546], ['spfh_2m2_1', 0.9611823078725884]]
['Columna: spfh_2m5_1', ['spfh_2m4_1', 0.9904992516406356], ['spfh_2m3_1', 0.9776313743577926]]
['Columna: tcdc_ea1_1', ['tcolc_e1_1', 0.9999825583915254]]
['Columna: tcdc_ea2_1', ['tcolc_e2_1', 0.9999840221035152]]
['Columna: tcdc_ea3_1', ['tcolc_e3_1', 0.9999844120529383]]
['Columna: tcdc_ea4_1', ['tcolc_e4_1', 0.9999840781440231]]
['Columna: tcdc_ea5_1', ['tcolc_e5_1', 0.9999743334851172]]
['Columna: tcolc_e1_1', ['tcdc_ea1_1', 0.9999825583915254]]
['Columna: tcolc_e2_1', ['tcdc_ea2_1', 0.9999840221035152]]
['Columna: tcolc_e3_1', ['tcdc_ea3_1', 0.9999844120529383]]
['Columna: tcolc_e4_1', ['tcdc_ea4_1', 0.9999840781440231]]
['Columna: tcolc_e5_1', ['tcdc_ea5_1', 0.9999743334851172]]
['Columna: tmax_2m1_1', ['ulwrf_s1_1', 0.9926185875344293], ['tmin_2m1_1', 0.9868798495796726], ['tmp_2m_1_1', 0.9848337078736275], ['tmp_sfc1_1', 0.9829175761904929], ['tmin_2m2_1', 0.9798377870127851], ['tmin_2m3_1', 0.9792576859569264], ['ulwrf_s2_1', 0.9711637882802264], ['tmax_2m2_1', 0.9639329994731967], ['tmp_2m_2_1', 0.9604125315224785], ['ulwrf_s3_1', 0.9578831233543978], ['tmp_sfc2_1', 0.952973392417377]]
['Columna: tmax_2m2_1', ['tmp_2m_2_1', 0.999279413581047], ['tmp_sfc2_1', 0.9963280066941894], ['ulwrf_s3_1', 0.9943735732483848], ['ulwrf_s2_1', 0.9916831918927983], ['tmax_2m3_1', 0.9863563274281074], ['tmp_2m_3_1', 0.9831085420291548], ['tmin_2m3_1', 0.9812920156142338], ['tmin_2m2_1', 0.9812757802653945], ['tmin_2m4_1', 0.9793670261955049], ['tmp_2m_1_1', 0.9764312479929851], ['tmin_2m1_1', 0.9727399736135504], ['tmin_2m5_1', 0.9716838103725776], ['tmax_2m4_1', 0.9702437599630711], ['tmax_2m5_1', 0.9701625450702344], ['tmp_sfc5_1', 0.9696966086282315], ['tmp_sfc1_1', 0.9686387404654087], ['tmp_2m_5_1', 0.968114400376335], ['tmp_2m_4_1', 0.9651306244123877], ['tmax_2m1_1', 0.9639329994731967], ['ulwrf_s1_1', 0.9602436099456786], ['ulwrf_s5_1', 0.9586701405643553], ['tmp_sfc3_1', 0.9539515582892754]]
['Columna: tmax_2m3_1', ['tmp_2m_3_1', 0.9990796906199355], ['tmin_2m4_1', 0.997488025624464], ['tmax_2m4_1', 0.9939478860387855], ['tmax_2m5_1', 0.9933934987034011], ['tmp_2m_4_1', 0.9905843118677575], ['ulwrf_s3_1', 0.98828872838694], ['tmp_2m_2_1', 0.9880036259862426], ['tmp_sfc2_1', 0.9875061204348023], ['tmax_2m2_1', 0.9863563274281074], ['tmin_2m5_1', 0.9849182247274321], ['tmp_sfc3_1', 0.9835345189022994], ['ulwrf_s5_1', 0.9833444127977561], ['tmp_2m_5_1', 0.9801602646803439], ['ulwrf_s4_1', 0.9784930183377477], ['tmp_sfc4_1', 0.9774159740457259], ['tmp_sfc5_1', 0.9752062916975955], ['ulwrf_s2_1', 0.9667463863072444], ['tmin_2m3_1', 0.9554251177759933], ['tmin_2m2_1', 0.9544379561079576]]
['Columna: tmax_2m4_1', ['tmax_2m5_1', 0.9998608411468725], ['tmp_2m_4_1', 0.9989991927452099], ['tmin_2m4_1', 0.996612948599106], ['tmp_2m_3_1', 0.9955799983150518], ['tmax_2m3_1', 0.9939478860387855], ['ulwrf_s5_1', 0.991573209428599], ['tmp_sfc4_1', 0.9902667320861007], ['tmin_2m5_1', 0.988699106486784], ['ulwrf_s4_1', 0.9869522366528207], ['tmp_2m_5_1', 0.9860993922018358], ['tmp_sfc3_1', 0.9856675762729512], ['tmp_sfc5_1', 0.9779255842920488], ['ulwrf_s3_1', 0.9761661994576958], ['tmp_sfc2_1', 0.9738550669787291], ['tmp_2m_2_1', 0.9734414112318843], ['tmax_2m2_1', 0.9702437599630711]]
['Columna: tmax_2m5_1', ['tmax_2m4_1', 0.9998608411468725], ['tmp_2m_4_1', 0.998912891511249], ['tmin_2m4_1', 0.9960374650259446], ['tmp_2m_3_1', 0.9949899182308278], ['tmax_2m3_1', 0.9933934987034011], ['ulwrf_s5_1', 0.9915012699578521], ['tmp_sfc4_1', 0.990183127468572], ['tmin_2m5_1', 0.9889690295147354], ['tmp_2m_5_1', 0.9870723795457379], ['ulwrf_s4_1', 0.986223408840029], ['tmp_sfc3_1', 0.9847393326351008], ['tmp_sfc5_1', 0.9789693477260845], ['ulwrf_s3_1', 0.9759211666401718], ['tmp_sfc2_1', 0.9737579994430984], ['tmp_2m_2_1', 0.9733709418009716], ['tmax_2m2_1', 0.9701625450702344]]
['Columna: tmin_2m1_1', ['tmp_2m_1_1', 0.9980817588892386], ['tmp_sfc1_1', 0.996248676153649], ['tmin_2m2_1', 0.9950124908861636], ['tmin_2m3_1', 0.9943605591613814], ['ulwrf_s1_1', 0.9927873948283475], ['tmax_2m1_1', 0.9868798495796726], ['ulwrf_s2_1', 0.9810615032837349], ['tmax_2m2_1', 0.9727399736135504], ['tmp_2m_2_1', 0.9700572344620553], ['ulwrf_s3_1', 0.9645067969204282], ['tmp_sfc2_1', 0.9596967032193685]]
['Columna: tmin_2m2_1', ['tmin_2m3_1', 0.9997062052233404], ['tmp_2m_1_1', 0.9981512005164516], ['tmp_sfc1_1', 0.9959410310876413], ['tmin_2m1_1', 0.9950124908861636], ['ulwrf_s2_1', 0.9897056135142127], ['ulwrf_s1_1', 0.9851587060862387], ['tmax_2m2_1', 0.9812757802653945], ['tmp_2m_2_1', 0.9802608446453747], ['tmax_2m1_1', 0.9798377870127851], ['ulwrf_s3_1', 0.9736090563981777], ['tmp_sfc2_1', 0.9702605319633663], ['tmax_2m3_1', 0.9544379561079576], ['tmp_2m_3_1', 0.9508499543680373]]
['Columna: tmin_2m3_1', ['tmin_2m2_1', 0.9997062052233404], ['tmp_2m_1_1', 0.9974641474499955], ['tmp_sfc1_1', 0.9951671659310295], ['tmin_2m1_1', 0.9943605591613814], ['ulwrf_s2_1', 0.9895084256536643], ['ulwrf_s1_1', 0.9845098225204798], ['tmax_2m2_1', 0.9812920156142338], ['tmp_2m_2_1', 0.9806732429028232], ['tmax_2m1_1', 0.9792576859569264], ['ulwrf_s3_1', 0.9743175328046926], ['tmp_sfc2_1', 0.9708140661560417], ['tmax_2m3_1', 0.9554251177759933], ['tmp_2m_3_1', 0.9527674304436178]]
['Columna: tmin_2m4_1', ['tmp_2m_3_1', 0.9989997829151602], ['tmax_2m3_1', 0.997488025624464], ['tmax_2m4_1', 0.996612948599106], ['tmax_2m5_1', 0.9960374650259446], ['tmp_2m_4_1', 0.9956331074283307], ['tmin_2m5_1', 0.9882923644581257], ['ulwrf_s5_1', 0.9881880630948019], ['tmp_sfc3_1', 0.9857595749820292], ['tmp_sfc4_1', 0.9840410123591383], ['ulwrf_s3_1', 0.983678745889547], ['tmp_2m_5_1', 0.9836322983829981], ['ulwrf_s4_1', 0.9836098106755029], ['tmp_2m_2_1', 0.9822198002917198], ['tmp_sfc2_1', 0.9822175846196547], ['tmax_2m2_1', 0.9793670261955049], ['tmp_sfc5_1', 0.9772816032898618], ['ulwrf_s2_1', 0.9589828757785641]]
['Columna: tmin_2m5_1', ['tmp_2m_5_1', 0.9985872418072574], ['tmp_sfc5_1', 0.9960878423266774], ['tmp_2m_4_1', 0.9893451023012511], ['tmax_2m5_1', 0.9889690295147354], ['tmax_2m4_1', 0.988699106486784], ['tmin_2m4_1', 0.9882923644581257], ['ulwrf_s5_1', 0.9871666099594831], ['tmp_2m_3_1', 0.9862414808572356], ['tmax_2m3_1', 0.9849182247274321], ['tmp_sfc4_1', 0.9798117195517193], ['ulwrf_s3_1', 0.97740751673737], ['tmp_sfc2_1', 0.975667677706812], ['tmp_2m_2_1', 0.9743545739437655], ['ulwrf_s4_1', 0.9733544288322069], ['tmax_2m2_1', 0.9716838103725776], ['tmp_sfc3_1', 0.9701100481890056], ['ulwrf_s2_1', 0.95891247038241]]
['Columna: tmp_2m_1_1', ['tmin_2m2_1', 0.9981512005164516], ['tmin_2m1_1', 0.9980817588892386], ['tmp_sfc1_1', 0.9979028208918925], ['tmin_2m3_1', 0.9974641474499955], ['ulwrf_s1_1', 0.989886382482919], ['ulwrf_s2_1', 0.9850396367967102], ['tmax_2m1_1', 0.9848337078736275], ['tmax_2m2_1', 0.9764312479929851], ['tmp_2m_2_1', 0.9737766982667028], ['ulwrf_s3_1', 0.9674427312184244], ['tmp_sfc2_1', 0.9630346941155181]]
['Columna: tmp_2m_2_1', ['tmax_2m2_1', 0.999279413581047], ['tmp_sfc2_1', 0.9971657687697132], ['ulwrf_s3_1', 0.9949020565647635], ['ulwrf_s2_1', 0.9905599295370681], ['tmax_2m3_1', 0.9880036259862426], ['tmp_2m_3_1', 0.9858190099460092], ['tmin_2m4_1', 0.9822198002917198], ['tmin_2m3_1', 0.9806732429028232], ['tmin_2m2_1', 0.9802608446453747], ['tmin_2m5_1', 0.9743545739437655], ['tmp_2m_1_1', 0.9737766982667028], ['tmax_2m4_1', 0.9734414112318843], ['tmax_2m5_1', 0.9733709418009716], ['tmp_sfc5_1', 0.9718603694854948], ['tmp_2m_5_1', 0.9707915853585791], ['tmin_2m1_1', 0.9700572344620553], ['tmp_2m_4_1', 0.9684540324625924], ['tmp_sfc1_1', 0.9657022955971822], ['ulwrf_s5_1', 0.9614582035068646], ['tmax_2m1_1', 0.9604125315224785], ['tmp_sfc3_1', 0.9570477759383318], ['ulwrf_s1_1', 0.9569062581410626], ['ulwrf_s4_1', 0.9500795033450581]]
['Columna: tmp_2m_3_1', ['tmax_2m3_1', 0.9990796906199355], ['tmin_2m4_1', 0.9989997829151602], ['tmax_2m4_1', 0.9955799983150518], ['tmax_2m5_1', 0.9949899182308278], ['tmp_2m_4_1', 0.992724635037048], ['ulwrf_s3_1', 0.9865819739434247], ['tmin_2m5_1', 0.9862414808572356], ['tmp_2m_2_1', 0.9858190099460092], ['tmp_sfc2_1', 0.9855607986565933], ['tmp_sfc3_1', 0.9854405898475033], ['ulwrf_s5_1', 0.9851699795124875], ['tmax_2m2_1', 0.9831085420291548], ['tmp_2m_5_1', 0.9814018293134269], ['ulwrf_s4_1', 0.980720770775525], ['tmp_sfc4_1', 0.9798660505699089], ['tmp_sfc5_1', 0.9758661885333627], ['ulwrf_s2_1', 0.9631815470434164], ['tmin_2m3_1', 0.9527674304436178], ['tmin_2m2_1', 0.9508499543680373]]
['Columna: tmp_2m_4_1', ['tmax_2m4_1', 0.9989991927452099], ['tmax_2m5_1', 0.998912891511249], ['tmin_2m4_1', 0.9956331074283307], ['tmp_2m_3_1', 0.992724635037048], ['ulwrf_s5_1', 0.992629010533321], ['tmp_sfc4_1', 0.9924065636207563], ['tmax_2m3_1', 0.9905843118677575], ['tmin_2m5_1', 0.9893451023012511], ['ulwrf_s4_1', 0.9874429299949301], ['tmp_2m_5_1', 0.9869728798681838], ['tmp_sfc3_1', 0.9838167456994329], ['tmp_sfc5_1', 0.9780204837374045], ['ulwrf_s3_1', 0.9717248352123742], ['tmp_sfc2_1', 0.9690993877999895], ['tmp_2m_2_1', 0.9684540324625924], ['tmax_2m2_1', 0.9651306244123877]]
['Columna: tmp_2m_5_1', ['tmin_2m5_1', 0.9985872418072574], ['tmp_sfc5_1', 0.9980210586015835], ['tmax_2m5_1', 0.9870723795457379], ['tmp_2m_4_1', 0.9869728798681838], ['tmax_2m4_1', 0.9860993922018358], ['ulwrf_s5_1', 0.9850429023316406], ['tmin_2m4_1', 0.9836322983829981], ['tmp_2m_3_1', 0.9814018293134269], ['tmax_2m3_1', 0.9801602646803439], ['tmp_sfc4_1', 0.9778991463220804], ['ulwrf_s3_1', 0.9735380279630496], ['tmp_sfc2_1', 0.972250922704137], ['tmp_2m_2_1', 0.9707915853585791], ['ulwrf_s4_1', 0.9690610099247569], ['tmax_2m2_1', 0.968114400376335], ['tmp_sfc3_1', 0.9643712722245484], ['ulwrf_s2_1', 0.956704311106679]]
['Columna: tmp_sfc1_1', ['tmp_2m_1_1', 0.9979028208918925], ['tmin_2m1_1', 0.996248676153649], ['tmin_2m2_1', 0.9959410310876413], ['tmin_2m3_1', 0.9951671659310295], ['ulwrf_s1_1', 0.9920525875016104], ['ulwrf_s2_1', 0.9836941167558975], ['tmax_2m1_1', 0.9829175761904929], ['tmax_2m2_1', 0.9686387404654087], ['tmp_2m_2_1', 0.9657022955971822], ['ulwrf_s3_1', 0.9608809573919886], ['tmp_sfc2_1', 0.9559322864982573]]
['Columna: tmp_sfc2_1', ['tmp_2m_2_1', 0.9971657687697132], ['ulwrf_s3_1', 0.9968919715831992], ['tmax_2m2_1', 0.9963280066941894], ['ulwrf_s2_1', 0.9877897069957257], ['tmax_2m3_1', 0.9875061204348023], ['tmp_2m_3_1', 0.9855607986565933], ['tmin_2m4_1', 0.9822175846196547], ['tmin_2m5_1', 0.975667677706812], ['tmp_sfc5_1', 0.9743551770315393], ['tmax_2m4_1', 0.9738550669787291], ['tmax_2m5_1', 0.9737579994430984], ['tmp_2m_5_1', 0.972250922704137], ['tmin_2m3_1', 0.9708140661560417], ['tmin_2m2_1', 0.9702605319633663], ['tmp_2m_4_1', 0.9690993877999895], ['ulwrf_s5_1', 0.967649863178039], ['tmp_sfc3_1', 0.9651682330506755], ['tmp_2m_1_1', 0.9630346941155181], ['tmin_2m1_1', 0.9596967032193685], ['ulwrf_s4_1', 0.9576549973115905], ['tmp_sfc4_1', 0.9563060096539977], ['tmp_sfc1_1', 0.9559322864982573], ['tmax_2m1_1', 0.952973392417377]]
['Columna: tmp_sfc3_1', ['ulwrf_s4_1', 0.9949082962956732], ['ulwrf_s5_1', 0.9894265289354863], ['tmp_sfc4_1', 0.9886690618484514], ['tmin_2m4_1', 0.9857595749820292], ['tmax_2m4_1', 0.9856675762729512], ['tmp_2m_3_1', 0.9854405898475033], ['tmax_2m5_1', 0.9847393326351008], ['tmp_2m_4_1', 0.9838167456994329], ['tmax_2m3_1', 0.9835345189022994], ['tmin_2m5_1', 0.9701100481890056], ['ulwrf_s3_1', 0.9698262276074615], ['tmp_sfc2_1', 0.9651682330506755], ['tmp_2m_5_1', 0.9643712722245484], ['tmp_2m_2_1', 0.9570477759383318], ['tmp_sfc5_1', 0.9560370173037632], ['tmax_2m2_1', 0.9539515582892754]]
['Columna: tmp_sfc4_1', ['ulwrf_s5_1', 0.9966804863664394], ['ulwrf_s4_1', 0.9957608658788498], ['tmp_2m_4_1', 0.9924065636207563], ['tmax_2m4_1', 0.9902667320861007], ['tmax_2m5_1', 0.990183127468572], ['tmp_sfc3_1', 0.9886690618484514], ['tmin_2m4_1', 0.9840410123591383], ['tmp_2m_3_1', 0.9798660505699089], ['tmin_2m5_1', 0.9798117195517193], ['tmp_2m_5_1', 0.9778991463220804], ['tmax_2m3_1', 0.9774159740457259], ['tmp_sfc5_1', 0.9685872464444223], ['ulwrf_s3_1', 0.9609986923315169], ['tmp_sfc2_1', 0.9563060096539977]]
['Columna: tmp_sfc5_1', ['tmp_2m_5_1', 0.9980210586015835], ['tmin_2m5_1', 0.9960878423266774], ['tmax_2m5_1', 0.9789693477260845], ['ulwrf_s5_1', 0.9787198905527447], ['tmp_2m_4_1', 0.9780204837374045], ['tmax_2m4_1', 0.9779255842920488], ['tmin_2m4_1', 0.9772816032898618], ['tmp_2m_3_1', 0.9758661885333627], ['tmax_2m3_1', 0.9752062916975955], ['ulwrf_s3_1', 0.9746737419558961], ['tmp_sfc2_1', 0.9743551770315393], ['tmp_2m_2_1', 0.9718603694854948], ['tmax_2m2_1', 0.9696966086282315], ['tmp_sfc4_1', 0.9685872464444223], ['ulwrf_s2_1', 0.9624211939860853], ['ulwrf_s4_1', 0.9600455484407057], ['tmp_sfc3_1', 0.9560370173037632]]
['Columna: ulwrf_s1_1', ['tmin_2m1_1', 0.9927873948283475], ['tmax_2m1_1', 0.9926185875344293], ['tmp_sfc1_1', 0.9920525875016104], ['tmp_2m_1_1', 0.989886382482919], ['tmin_2m2_1', 0.9851587060862387], ['tmin_2m3_1', 0.9845098225204798], ['ulwrf_s2_1', 0.9761880750421632], ['tmax_2m2_1', 0.9602436099456786], ['tmp_2m_2_1', 0.9569062581410626], ['ulwrf_s3_1', 0.9568053797580687]]
['Columna: ulwrf_s2_1', ['tmax_2m2_1', 0.9916831918927983], ['tmp_2m_2_1', 0.9905599295370681], ['ulwrf_s3_1', 0.9904697536560035], ['tmin_2m2_1', 0.9897056135142127], ['tmin_2m3_1', 0.9895084256536643], ['tmp_sfc2_1', 0.9877897069957257], ['tmp_2m_1_1', 0.9850396367967102], ['tmp_sfc1_1', 0.9836941167558975], ['tmin_2m1_1', 0.9810615032837349], ['ulwrf_s1_1', 0.9761880750421632], ['tmax_2m1_1', 0.9711637882802264], ['tmax_2m3_1', 0.9667463863072444], ['tmp_2m_3_1', 0.9631815470434164], ['tmp_sfc5_1', 0.9624211939860853], ['tmin_2m4_1', 0.9589828757785641], ['tmin_2m5_1', 0.95891247038241], ['tmp_2m_5_1', 0.956704311106679]]
['Columna: ulwrf_s3_1', ['tmp_sfc2_1', 0.9968919715831992], ['tmp_2m_2_1', 0.9949020565647635], ['tmax_2m2_1', 0.9943735732483848], ['ulwrf_s2_1', 0.9904697536560035], ['tmax_2m3_1', 0.98828872838694], ['tmp_2m_3_1', 0.9865819739434247], ['tmin_2m4_1', 0.983678745889547], ['tmin_2m5_1', 0.97740751673737], ['tmax_2m4_1', 0.9761661994576958], ['tmax_2m5_1', 0.9759211666401718], ['tmp_sfc5_1', 0.9746737419558961], ['tmin_2m3_1', 0.9743175328046926], ['ulwrf_s5_1', 0.9738021131102192], ['tmin_2m2_1', 0.9736090563981777], ['tmp_2m_5_1', 0.9735380279630496], ['tmp_2m_4_1', 0.9717248352123742], ['tmp_sfc3_1', 0.9698262276074615], ['tmp_2m_1_1', 0.9674427312184244], ['ulwrf_s4_1', 0.9653517574338257], ['tmin_2m1_1', 0.9645067969204282], ['tmp_sfc4_1', 0.9609986923315169], ['tmp_sfc1_1', 0.9608809573919886], ['tmax_2m1_1', 0.9578831233543978], ['ulwrf_s1_1', 0.9568053797580687]]
['Columna: ulwrf_s4_1', ['ulwrf_s5_1', 0.9963102550341579], ['tmp_sfc4_1', 0.9957608658788498], ['tmp_sfc3_1', 0.9949082962956732], ['tmp_2m_4_1', 0.9874429299949301], ['tmax_2m4_1', 0.9869522366528207], ['tmax_2m5_1', 0.986223408840029], ['tmin_2m4_1', 0.9836098106755029], ['tmp_2m_3_1', 0.980720770775525], ['tmax_2m3_1', 0.9784930183377477], ['tmin_2m5_1', 0.9733544288322069], ['tmp_2m_5_1', 0.9690610099247569], ['ulwrf_s3_1', 0.9653517574338257], ['tmp_sfc5_1', 0.9600455484407057], ['tmp_sfc2_1', 0.9576549973115905], ['tmp_2m_2_1', 0.9500795033450581]]
['Columna: ulwrf_s5_1', ['tmp_sfc4_1', 0.9966804863664394], ['ulwrf_s4_1', 0.9963102550341579], ['tmp_2m_4_1', 0.992629010533321], ['tmax_2m4_1', 0.991573209428599], ['tmax_2m5_1', 0.9915012699578521], ['tmp_sfc3_1', 0.9894265289354863], ['tmin_2m4_1', 0.9881880630948019], ['tmin_2m5_1', 0.9871666099594831], ['tmp_2m_3_1', 0.9851699795124875], ['tmp_2m_5_1', 0.9850429023316406], ['tmax_2m3_1', 0.9833444127977561], ['tmp_sfc5_1', 0.9787198905527447], ['ulwrf_s3_1', 0.9738021131102192], ['tmp_sfc2_1', 0.967649863178039], ['tmp_2m_2_1', 0.9614582035068646], ['tmax_2m2_1', 0.9586701405643553]]
['Columna: ulwrf_t1_1']
['Columna: ulwrf_t2_1', ['ulwrf_t3_1', 0.9751600831406207]]
['Columna: ulwrf_t3_1', ['ulwrf_t2_1', 0.9751600831406207]]
['Columna: ulwrf_t4_1', ['ulwrf_t5_1', 0.9759671994957412]]
['Columna: ulwrf_t5_1', ['ulwrf_t4_1', 0.9759671994957412]]
['Columna: uswrf_s1_1']
['Columna: uswrf_s2_1', ['dswrf_s2_1', 0.9912815245133624], ['dswrf_s3_1', 0.9591543590605579]]
['Columna: uswrf_s3_1']
['Columna: uswrf_s4_1', ['uswrf_s5_1', 0.9567727958269349]]
['Columna: uswrf_s5_1', ['uswrf_s4_1', 0.9567727958269349]]
['Columna: salida']
[['Columna: apcp_sf1_1'], ['Columna: apcp_sf2_1'], ['Columna: apcp_sf3_1'], ['Columna: apcp_sf4_1'], ['Columna: apcp_sf5_1'], ['Columna: dlwrf_s1_1', ['dlwrf_s2_1', 0.9654602451282467], ['dlwrf_s3_1', 0.9555714275535937]], ['Columna: dlwrf_s2_1', ['dlwrf_s3_1', 0.993801937354884], ['dlwrf_s1_1', 0.9654602451282467]], ['Columna: dlwrf_s3_1', ['dlwrf_s2_1', 0.993801937354884], ['dlwrf_s4_1', 0.9664287191255341], ['dlwrf_s5_1', 0.955638710623432], ['dlwrf_s1_1', 0.9555714275535937]], ['Columna: dlwrf_s4_1', ['dlwrf_s5_1', 0.9968432736329301], ['dlwrf_s3_1', 0.9664287191255341]], ['Columna: dlwrf_s5_1', ['dlwrf_s4_1', 0.9968432736329301], ['dlwrf_s3_1', 0.955638710623432]], ['Columna: dswrf_s1_1'], ['Columna: dswrf_s2_1', ['uswrf_s2_1', 0.9912815245133624], ['dswrf_s3_1', 0.9503100565387964]], ['Columna: dswrf_s3_1', ['uswrf_s2_1', 0.9591543590605579], ['dswrf_s2_1', 0.9503100565387964]], ['Columna: dswrf_s4_1', ['dswrf_s5_1', 0.9825798647066863]], ['Columna: dswrf_s5_1', ['dswrf_s4_1', 0.9825798647066863]], ['Columna: pres_ms1_1', ['pres_ms2_1', 0.9880297011546728], ['pres_ms3_1', 0.9578904939482652]], ['Columna: pres_ms2_1', ['pres_ms1_1', 0.9880297011546728], ['pres_ms3_1', 0.9874219399163731], ['pres_ms4_1', 0.9552800186140463]], ['Columna: pres_ms3_1', ['pres_ms2_1', 0.9874219399163731], ['pres_ms4_1', 0.9871476057524855], ['pres_ms1_1', 0.9578904939482652], ['pres_ms5_1', 0.9555376601620624]], ['Columna: pres_ms4_1', ['pres_ms3_1', 0.9871476057524855], ['pres_ms5_1', 0.9857526889691135], ['pres_ms2_1', 0.9552800186140463]], ['Columna: pres_ms5_1', ['pres_ms4_1', 0.9857526889691135], ['pres_ms3_1', 0.9555376601620624]], ['Columna: pwat_ea1_1', ['pwat_ea2_1', 0.9859162230035117], ['pwat_ea3_1', 0.9579571492658394]], ['Columna: pwat_ea2_1', ['pwat_ea3_1', 0.987435297363512], ['pwat_ea1_1', 0.9859162230035117], ['pwat_ea4_1', 0.9616516049128513]], ['Columna: pwat_ea3_1', ['pwat_ea4_1', 0.9880573471002622], ['pwat_ea2_1', 0.987435297363512], ['pwat_ea5_1', 0.9616115768986265], ['pwat_ea1_1', 0.9579571492658394]], ['Columna: pwat_ea4_1', ['pwat_ea3_1', 0.9880573471002622], ['pwat_ea5_1', 0.9867846903534372], ['pwat_ea2_1', 0.9616516049128513]], ['Columna: pwat_ea5_1', ['pwat_ea4_1', 0.9867846903534372], ['pwat_ea3_1', 0.9616115768986265]], ['Columna: spfh_2m1_1', ['spfh_2m2_1', 0.9740287040850961]], ['Columna: spfh_2m2_1', ['spfh_2m3_1', 0.9851324272253641], ['spfh_2m1_1', 0.9740287040850961], ['spfh_2m4_1', 0.9611823078725884]], ['Columna: spfh_2m3_1', ['spfh_2m4_1', 0.9893999492368546], ['spfh_2m2_1', 0.9851324272253641], ['spfh_2m5_1', 0.9776313743577926]], ['Columna: spfh_2m4_1', ['spfh_2m5_1', 0.9904992516406356], ['spfh_2m3_1', 0.9893999492368546], ['spfh_2m2_1', 0.9611823078725884]], ['Columna: spfh_2m5_1', ['spfh_2m4_1', 0.9904992516406356], ['spfh_2m3_1', 0.9776313743577926]], ['Columna: tcdc_ea1_1', ['tcolc_e1_1', 0.9999825583915254]], ['Columna: tcdc_ea2_1', ['tcolc_e2_1', 0.9999840221035152]], ['Columna: tcdc_ea3_1', ['tcolc_e3_1', 0.9999844120529383]], ['Columna: tcdc_ea4_1', ['tcolc_e4_1', 0.9999840781440231]], ['Columna: tcdc_ea5_1', ['tcolc_e5_1', 0.9999743334851172]], ['Columna: tcolc_e1_1', ['tcdc_ea1_1', 0.9999825583915254]], ['Columna: tcolc_e2_1', ['tcdc_ea2_1', 0.9999840221035152]], ['Columna: tcolc_e3_1', ['tcdc_ea3_1', 0.9999844120529383]], ['Columna: tcolc_e4_1', ['tcdc_ea4_1', 0.9999840781440231]], ['Columna: tcolc_e5_1', ['tcdc_ea5_1', 0.9999743334851172]], ['Columna: tmax_2m1_1', ['ulwrf_s1_1', 0.9926185875344293], ['tmin_2m1_1', 0.9868798495796726], ['tmp_2m_1_1', 0.9848337078736275], ['tmp_sfc1_1', 0.9829175761904929], ['tmin_2m2_1', 0.9798377870127851], ['tmin_2m3_1', 0.9792576859569264], ['ulwrf_s2_1', 0.9711637882802264], ['tmax_2m2_1', 0.9639329994731967], ['tmp_2m_2_1', 0.9604125315224785], ['ulwrf_s3_1', 0.9578831233543978], ['tmp_sfc2_1', 0.952973392417377]], ['Columna: tmax_2m2_1', ['tmp_2m_2_1', 0.999279413581047], ['tmp_sfc2_1', 0.9963280066941894], ['ulwrf_s3_1', 0.9943735732483848], ['ulwrf_s2_1', 0.9916831918927983], ['tmax_2m3_1', 0.9863563274281074], ['tmp_2m_3_1', 0.9831085420291548], ['tmin_2m3_1', 0.9812920156142338], ['tmin_2m2_1', 0.9812757802653945], ['tmin_2m4_1', 0.9793670261955049], ['tmp_2m_1_1', 0.9764312479929851], ['tmin_2m1_1', 0.9727399736135504], ['tmin_2m5_1', 0.9716838103725776], ['tmax_2m4_1', 0.9702437599630711], ['tmax_2m5_1', 0.9701625450702344], ['tmp_sfc5_1', 0.9696966086282315], ['tmp_sfc1_1', 0.9686387404654087], ['tmp_2m_5_1', 0.968114400376335], ['tmp_2m_4_1', 0.9651306244123877], ['tmax_2m1_1', 0.9639329994731967], ['ulwrf_s1_1', 0.9602436099456786], ['ulwrf_s5_1', 0.9586701405643553], ['tmp_sfc3_1', 0.9539515582892754]], ['Columna: tmax_2m3_1', ['tmp_2m_3_1', 0.9990796906199355], ['tmin_2m4_1', 0.997488025624464], ['tmax_2m4_1', 0.9939478860387855], ['tmax_2m5_1', 0.9933934987034011], ['tmp_2m_4_1', 0.9905843118677575], ['ulwrf_s3_1', 0.98828872838694], ['tmp_2m_2_1', 0.9880036259862426], ['tmp_sfc2_1', 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tmp_sfc5_1', ['tmp_2m_5_1', 0.9980210586015835], ['tmin_2m5_1', 0.9960878423266774], ['tmax_2m5_1', 0.9789693477260845], ['ulwrf_s5_1', 0.9787198905527447], ['tmp_2m_4_1', 0.9780204837374045], ['tmax_2m4_1', 0.9779255842920488], ['tmin_2m4_1', 0.9772816032898618], ['tmp_2m_3_1', 0.9758661885333627], ['tmax_2m3_1', 0.9752062916975955], ['ulwrf_s3_1', 0.9746737419558961], ['tmp_sfc2_1', 0.9743551770315393], ['tmp_2m_2_1', 0.9718603694854948], ['tmax_2m2_1', 0.9696966086282315], ['tmp_sfc4_1', 0.9685872464444223], ['ulwrf_s2_1', 0.9624211939860853], ['ulwrf_s4_1', 0.9600455484407057], ['tmp_sfc3_1', 0.9560370173037632]], ['Columna: ulwrf_s1_1', ['tmin_2m1_1', 0.9927873948283475], ['tmax_2m1_1', 0.9926185875344293], ['tmp_sfc1_1', 0.9920525875016104], ['tmp_2m_1_1', 0.989886382482919], ['tmin_2m2_1', 0.9851587060862387], ['tmin_2m3_1', 0.9845098225204798], ['ulwrf_s2_1', 0.9761880750421632], ['tmax_2m2_1', 0.9602436099456786], ['tmp_2m_2_1', 0.9569062581410626], ['ulwrf_s3_1', 0.9568053797580687]], ['Columna: ulwrf_s2_1', ['tmax_2m2_1', 0.9916831918927983], ['tmp_2m_2_1', 0.9905599295370681], ['ulwrf_s3_1', 0.9904697536560035], ['tmin_2m2_1', 0.9897056135142127], ['tmin_2m3_1', 0.9895084256536643], ['tmp_sfc2_1', 0.9877897069957257], ['tmp_2m_1_1', 0.9850396367967102], ['tmp_sfc1_1', 0.9836941167558975], ['tmin_2m1_1', 0.9810615032837349], ['ulwrf_s1_1', 0.9761880750421632], ['tmax_2m1_1', 0.9711637882802264], ['tmax_2m3_1', 0.9667463863072444], ['tmp_2m_3_1', 0.9631815470434164], ['tmp_sfc5_1', 0.9624211939860853], ['tmin_2m4_1', 0.9589828757785641], ['tmin_2m5_1', 0.95891247038241], ['tmp_2m_5_1', 0.956704311106679]], ['Columna: ulwrf_s3_1', ['tmp_sfc2_1', 0.9968919715831992], ['tmp_2m_2_1', 0.9949020565647635], ['tmax_2m2_1', 0.9943735732483848], ['ulwrf_s2_1', 0.9904697536560035], ['tmax_2m3_1', 0.98828872838694], ['tmp_2m_3_1', 0.9865819739434247], ['tmin_2m4_1', 0.983678745889547], ['tmin_2m5_1', 0.97740751673737], ['tmax_2m4_1', 0.9761661994576958], ['tmax_2m5_1', 0.9759211666401718], ['tmp_sfc5_1', 0.9746737419558961], ['tmin_2m3_1', 0.9743175328046926], ['ulwrf_s5_1', 0.9738021131102192], ['tmin_2m2_1', 0.9736090563981777], ['tmp_2m_5_1', 0.9735380279630496], ['tmp_2m_4_1', 0.9717248352123742], ['tmp_sfc3_1', 0.9698262276074615], ['tmp_2m_1_1', 0.9674427312184244], ['ulwrf_s4_1', 0.9653517574338257], ['tmin_2m1_1', 0.9645067969204282], ['tmp_sfc4_1', 0.9609986923315169], ['tmp_sfc1_1', 0.9608809573919886], ['tmax_2m1_1', 0.9578831233543978], ['ulwrf_s1_1', 0.9568053797580687]], ['Columna: ulwrf_s4_1', ['ulwrf_s5_1', 0.9963102550341579], ['tmp_sfc4_1', 0.9957608658788498], ['tmp_sfc3_1', 0.9949082962956732], ['tmp_2m_4_1', 0.9874429299949301], ['tmax_2m4_1', 0.9869522366528207], ['tmax_2m5_1', 0.986223408840029], ['tmin_2m4_1', 0.9836098106755029], ['tmp_2m_3_1', 0.980720770775525], ['tmax_2m3_1', 0.9784930183377477], ['tmin_2m5_1', 0.9733544288322069], ['tmp_2m_5_1', 0.9690610099247569], ['ulwrf_s3_1', 0.9653517574338257], ['tmp_sfc5_1', 0.9600455484407057], ['tmp_sfc2_1', 0.9576549973115905], ['tmp_2m_2_1', 0.9500795033450581]], ['Columna: ulwrf_s5_1', ['tmp_sfc4_1', 0.9966804863664394], ['ulwrf_s4_1', 0.9963102550341579], ['tmp_2m_4_1', 0.992629010533321], ['tmax_2m4_1', 0.991573209428599], ['tmax_2m5_1', 0.9915012699578521], ['tmp_sfc3_1', 0.9894265289354863], ['tmin_2m4_1', 0.9881880630948019], ['tmin_2m5_1', 0.9871666099594831], ['tmp_2m_3_1', 0.9851699795124875], ['tmp_2m_5_1', 0.9850429023316406], ['tmax_2m3_1', 0.9833444127977561], ['tmp_sfc5_1', 0.9787198905527447], ['ulwrf_s3_1', 0.9738021131102192], ['tmp_sfc2_1', 0.967649863178039], ['tmp_2m_2_1', 0.9614582035068646], ['tmax_2m2_1', 0.9586701405643553]], ['Columna: ulwrf_t1_1'], ['Columna: ulwrf_t2_1', ['ulwrf_t3_1', 0.9751600831406207]], ['Columna: ulwrf_t3_1', ['ulwrf_t2_1', 0.9751600831406207]], ['Columna: ulwrf_t4_1', ['ulwrf_t5_1', 0.9759671994957412]], ['Columna: ulwrf_t5_1', ['ulwrf_t4_1', 0.9759671994957412]], ['Columna: uswrf_s1_1'], ['Columna: uswrf_s2_1', ['dswrf_s2_1', 0.9912815245133624], ['dswrf_s3_1', 0.9591543590605579]], ['Columna: uswrf_s3_1'], ['Columna: uswrf_s4_1', ['uswrf_s5_1', 0.9567727958269349]], ['Columna: uswrf_s5_1', ['uswrf_s4_1', 0.9567727958269349]], ['Columna: salida']]


""" seaborne Correlation Heat Map """

# It needs to show all the columns
fig, ax = plt.subplots(figsize=(19,18))

plt.title('Correlation Heat Map',y=1)
# We use blue color scale because it is easier to see the annotations and the correlation values
sns.heatmap(correlation, square=True, cmap='Blues', annot=True, fmt='.2f', annot_kws={'size': 4}, linewidths=.3, cbar_kws={"shrink": .5}, vmin=0.0, vmax=1)
# We can modify vmax=0.95 in order to get same color scale for values with more than 0.95 correlation
# Note: it delays around 15 seconds as it needs to plot a 76*76 matrix with its 5766 correlation values

# Exporting image as png to ../data/img folder - easier to visualize the annotations, better resolution
plt.savefig("../data/img/correlation_heatmap.png", dpi=200)


# 1

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['apcp_sf1_1', 'apcp_sf2_1', 'apcp_sf3_1','apcp_sf4_1', 'apcp_sf5_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 2

sns.set(), sns.set(font_scale=.75)
columns = []

columns = [ 'dlwrf_s1_1', 'dlwrf_s2_1', 'dlwrf_s3_1', 'dlwrf_s4_1', 'dlwrf_s5_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 3

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['pwat_ea1_1', 'pwat_ea2_1','pwat_ea3_1','pwat_ea4_1','pwat_ea5_1', 'dlwrf_s1_1', 'dlwrf_s2_1', 'dlwrf_s3_1', 'dlwrf_s4_1', 'dlwrf_s5_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 4

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['dswrf_s1_1', 'dswrf_s2_1', 'dswrf_s3_1', 'dswrf_s4_1', 'dswrf_s5_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 5

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['dswrf_s1_1', 'dswrf_s2_1', 'dswrf_s3_1', 'dswrf_s4_1', 'dswrf_s5_1', 'uswrf_s1_1', 'uswrf_s2_1', 'uswrf_s3_1', 'uswrf_s4_1', 'uswrf_s5_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 6

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['pres_ms1_1', 'pres_ms2_1', 'pres_ms3_1', 'pres_ms4_1', 'pres_ms5_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 7

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['pwat_ea1_1', 'pwat_ea2_1','pwat_ea3_1','pwat_ea4_1','pwat_ea5_1', 'spfh_2m1_1', 'spfh_2m2_1', 'spfh_2m3_1', 'spfh_2m4_1', 'spfh_2m5_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 8

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['spfh_2m1_1', 'spfh_2m2_1', 'spfh_2m3_1', 'spfh_2m4_1', 'spfh_2m5_1','ulwrf_s1_1', 'ulwrf_s2_1', 'ulwrf_s3_1', 'ulwrf_s4_1', 'ulwrf_s5_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 9

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['tmax_2m1_1', 'tmax_2m2_1', 'tmax_2m3_1', 'tmax_2m4_1', 'tmax_2m5_1', 'tmin_2m1_1', 'tmin_2m2_1', 'tmin_2m3_1', 'tmin_2m4_1', 'tmin_2m5_1','tmp_2m_1_1', 'tmp_2m_2_1', 'tmp_2m_3_1', 'tmp_2m_4_1', 'tmp_2m_5_1', 'tmp_sfc1_1', 'tmp_sfc2_1', 'tmp_sfc3_1', 'tmp_sfc4_1', 'tmp_sfc5_1','ulwrf_s1_1', 'ulwrf_s2_1', 'ulwrf_s3_1', 'ulwrf_s4_1', 'ulwrf_s5_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 10

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['ulwrf_t1_1', 'ulwrf_t2_1', 'ulwrf_t3_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 11

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['ulwrf_t4_1', 'ulwrf_t5_1', ]

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


# 12

sns.set(), sns.set(font_scale=.75)
columns = []

columns = ['uswrf_s2_1', 'uswrf_s3_1', 'uswrf_s4_1', 'uswrf_s5_1']

sns.pairplot(disp_df[columns], size = 1 ,kind ='scatter',diag_kind='kde')
plt.show()

/home/carlosiborra/anaconda3/envs/ml_practica1_env/lib/python3.9/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)


""" Train Test Split (time series) """

# * Make a copy of the dataframe (as Padas dataframe is mutable, therefore uses a reference)
disp_df_copy = disp_df.copy()

# print(disp_df)
# print(disp_df_copy)

# Now we make the train_x, train_y, test_x, test_y splits taking into account the time series
# Note: the time series is ordered by date, therefore we need to split the data in a way that the train data is before the test data
# Note: the 10 first years are used for training and the last two years for testing
# Note: this is done because if not, we will be predicting the past from the future, which leads to errors and overfitting (data leakage) in the model

# * Calculate the number of rows for training and testing
num_rows = disp_df_copy.shape[0]
num_train_rows = int(num_rows * 10/12)  # 10 first years for training, 2 last years for testing

# * Split the data into train and test dataframes (using iloc instead of train_test_split as it picks random rows)
train_df = disp_df_copy.iloc[:num_train_rows, :]  # train contains the first 10 years of rows
test_df = disp_df_copy.iloc[num_train_rows:, :] # test contains the last 2 years of rows

# Print the number of rows for each dataframe
print(f"Number of rows for training: {train_df.shape[0]}")
print(f"Number of rows for testing: {test_df.shape[0]}")

# Print the dataframes
# print(train_df), print(test_df)

# * Separate the input features and target variable for training and testing
train_x = train_df.drop('salida', axis=1)   # By using drop we create a new dataframe without the column 'salida' (train)
train_y = train_df['salida']                # We create a new dataframe with only the column 'salida' (train)
test_x = test_df.drop('salida', axis=1)     # By using drop we create a new dataframe without the column 'salida' (test)
test_y = test_df['salida']                  # We create a new dataframe with only the column 'salida' (test)

# Print the shapes of the dataframes
print(train_x.shape, train_y.shape, test_x.shape, test_y.shape)

Number of rows for training: 3650
Number of rows for testing: 730
(3650, 75) (3650,) (730, 75) (730,)


from nbconvert import HTMLExporter

# Create an HTMLExporter object
html_exporter = HTMLExporter()

# Notebook path
path = "./model.ipynb"

# Get the current notebook's filename
notebook_filename = os.path.splitext(os.path.basename(path))[0]

# Export the notebook to an HTML file
(body, resources) = html_exporter.from_filename(path)
html_file = "../data/html/" + notebook_filename + ".html"
with open(html_file, "w", encoding="utf-8") as f:
    f.write(body)
    
print("Notebook exported to HTML:", html_file)

Notebook exported to HTML: model.html

	apcp_sf1_1	apcp_sf2_1	apcp_sf3_1	apcp_sf4_1	apcp_sf5_1	dlwrf_s1_1	dlwrf_s2_1	dlwrf_s3_1	dlwrf_s4_1	dlwrf_s5_1	dswrf_s1_1	dswrf_s2_1	dswrf_s3_1	dswrf_s4_1	dswrf_s5_1	pres_ms1_1	pres_ms2_1	pres_ms3_1	pres_ms4_1	pres_ms5_1	pwat_ea1_1	pwat_ea2_1	pwat_ea3_1	pwat_ea4_1	pwat_ea5_1	spfh_2m1_1	spfh_2m2_1	spfh_2m3_1	spfh_2m4_1	spfh_2m5_1	tcdc_ea1_1	tcdc_ea2_1	tcdc_ea3_1	tcdc_ea4_1	tcdc_ea5_1	tcolc_e1_1	tcolc_e2_1	tcolc_e3_1	tcolc_e4_1	tcolc_e5_1	tmax_2m1_1	tmax_2m2_1	tmax_2m3_1	tmax_2m4_1	tmax_2m5_1	tmin_2m1_1	tmin_2m2_1	tmin_2m3_1	tmin_2m4_1	tmin_2m5_1	tmp_2m_1_1	tmp_2m_2_1	tmp_2m_3_1	tmp_2m_4_1	tmp_2m_5_1	tmp_sfc1_1	tmp_sfc2_1	tmp_sfc3_1	tmp_sfc4_1	tmp_sfc5_1	ulwrf_s1_1	ulwrf_s2_1	ulwrf_s3_1	ulwrf_s4_1	ulwrf_s5_1	ulwrf_t1_1	ulwrf_t2_1	ulwrf_t3_1	ulwrf_t4_1	ulwrf_t5_1	uswrf_s1_1	uswrf_s2_1	uswrf_s3_1	uswrf_s4_1	uswrf_s5_1	salida
count	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4.380000e+03
mean	0.606809	0.252764	0.480446	0.271476	0.514641	316.698532	317.381851	324.682998	343.757513	343.097254	0.070693	163.547696	375.982939	685.232316	507.620569	101724.224223	101779.516825	101746.997257	101542.138914	101503.806442	21.412453	21.577143	22.172867	22.628942	22.419481	0.007851	0.008863	0.009370	0.009483	0.009927	0.069813	0.069181	0.065218	0.065575	0.061610	0.070115	0.069504	0.065527	0.065919	0.061997	286.998221	288.368218	292.889562	294.567249	294.625310	284.644851	284.697383	284.678400	292.825082	291.176236	284.897279	288.305418	292.829753	294.385505	291.386512	284.142094	289.314751	295.635538	295.995843	290.450591	376.183645	382.345516	401.169657	439.634087	431.819393	247.683051	247.300145	251.597699	261.916025	260.870143	0.076235	38.585222	76.064674	126.504047	99.087049	1.629826e+07
std	2.275740	0.999809	1.774720	1.086599	1.886735	55.637609	57.874376	58.702133	60.860672	60.693451	0.295001	112.394328	159.517593	228.049707	193.860461	721.839730	728.790723	718.552527	698.496042	714.652866	12.136053	12.264956	12.495203	12.543698	12.310466	0.004374	0.005020	0.005165	0.005088	0.005442	0.167006	0.170252	0.169748	0.169143	0.162010	0.166901	0.170133	0.169632	0.169005	0.161853	8.851120	9.686553	9.827525	9.708510	9.696085	8.663294	8.796469	8.800662	9.880096	10.026533	8.652794	9.738082	9.873836	9.716331	10.015781	8.794389	9.702728	9.070324	9.231690	10.402144	46.178756	49.619215	50.445754	52.695819	53.978436	36.353242	36.804219	36.382653	39.190955	38.734023	0.255491	25.984861	30.784078	40.571154	35.454368	7.965298e+06
min	0.000000	0.000000	0.000000	0.000000	0.000000	158.971770	160.032903	165.524543	183.671312	186.342961	0.000000	0.000000	20.000000	30.000000	20.000000	99316.970881	99097.036222	98946.524858	99010.708097	98830.153409	1.100000	1.314819	1.107352	1.142803	1.201246	0.000462	0.000485	0.000451	0.000478	0.000468	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	254.589220	254.937418	258.549777	260.800365	260.863475	251.941358	249.576132	249.576714	258.698331	258.171345	251.942065	254.844406	258.552646	260.795430	258.170049	250.100794	256.360800	263.634377	264.533564	256.520408	227.242239	223.985486	246.314349	278.576630	271.707606	104.671267	113.559602	118.679132	119.393449	121.951425	0.000000	0.000000	3.181818	4.363636	2.545455	5.100000e+05
25%	0.000000	0.000000	0.000000	0.000000	0.000000	271.219564	268.889864	275.709318	293.757163	292.834810	0.000000	52.727273	240.000000	524.545455	343.977273	101266.292436	101311.457564	101283.620206	101100.136186	101050.542436	10.971268	10.849190	11.235352	11.630796	11.657611	0.003998	0.004264	0.004628	0.004745	0.004817	0.000000	0.000000	0.000000	0.000000	0.000000	0.000627	0.000573	0.000545	0.000691	0.000736	280.341776	280.616793	285.221118	287.201786	287.274496	278.056773	277.877620	277.874818	285.055183	283.568461	278.246751	280.472266	285.052367	287.063607	283.701843	277.188510	281.579497	288.821357	289.178255	282.239473	339.283260	341.152538	359.526828	399.347294	389.547691	230.499519	230.482578	233.976283	245.721419	243.850282	0.000000	14.000000	53.431818	108.454545	74.795455	1.062375e+07
50%	0.000000	0.000000	0.000000	0.000000	0.000000	321.106932	323.063311	329.708327	346.763388	345.883918	0.000000	149.090909	382.000000	725.454545	524.954545	101655.984730	101712.774503	101680.867188	101479.565696	101430.512784	19.312292	19.302599	19.763636	20.431517	20.389514	0.007257	0.008292	0.008952	0.009197	0.009563	0.005455	0.004545	0.003636	0.003636	0.003636	0.005150	0.004959	0.004136	0.004514	0.003759	287.690272	289.161910	293.823814	295.576867	295.664069	285.190432	285.319819	285.210710	293.664572	291.892625	285.524544	289.075811	293.796660	295.402143	292.148650	284.734411	290.105994	296.272095	296.772838	291.155626	376.667526	383.600055	401.964017	441.313997	434.092922	253.198004	253.309217	257.270334	270.790095	269.300432	0.000000	35.363636	79.272727	135.909091	104.818182	1.635690e+07
75%	0.109091	0.051818	0.127500	0.036364	0.090909	366.145476	370.147353	378.509126	399.600853	398.728281	0.000000	262.727273	522.545455	892.045455	691.750000	102141.220348	102200.644176	102152.131214	101943.061435	101919.519709	30.959091	31.400947	32.354219	32.975179	32.373798	0.011580	0.013512	0.014259	0.014150	0.015061	0.056364	0.059091	0.044545	0.045455	0.039091	0.056350	0.059170	0.044798	0.044630	0.039120	294.395465	296.994538	301.447272	302.796303	302.828929	292.226128	292.565418	292.550144	301.434116	299.956680	292.520470	296.991716	301.433807	302.711204	300.108120	292.049802	297.934257	303.308531	303.679695	299.685545	416.400996	428.011458	445.903448	483.390056	477.311875	274.555647	274.861445	278.800420	290.139446	289.699652	0.000000	61.818182	102.727273	155.204545	129.545455	2.312775e+07
max	43.009090	16.846364	28.399091	26.381818	36.875455	426.173970	427.486894	429.693146	455.566337	453.910406	3.000000	381.818182	643.545455	990.000000	791.090909	104688.396307	104856.285511	104693.185369	104244.932528	104249.968040	60.327273	58.876881	59.915362	59.309182	60.529133	0.018809	0.019533	0.020985	0.021932	0.023318	1.920909	2.370000	2.449091	2.146364	1.957273	1.920136	2.369282	2.450482	2.146409	1.956655	304.480122	304.792880	311.277519	312.660564	312.668726	300.350930	299.724509	299.735546	310.815957	308.761763	300.344230	304.773410	311.272270	312.595520	309.005149	299.869093	306.834309	315.964081	313.965757	308.770635	470.753102	469.429213	504.584351	555.704024	542.529280	318.245345	311.991660	315.569164	328.920274	327.253141	1.000000	92.272727	192.636364	450.636364	313.909091	3.122700e+07

	apcp_sf4_1	apcp_sf5_1	dlwrf_s1_1	dlwrf_s2_1	dlwrf_s3_1	dlwrf_s4_1	dlwrf_s5_1	dswrf_s2_1	dswrf_s3_1	dswrf_s4_1	dswrf_s5_1	pres_ms1_1	pres_ms2_1	pres_ms3_1	pres_ms4_1	pres_ms5_1	pwat_ea1_1	pwat_ea2_1	pwat_ea3_1	pwat_ea4_1	pwat_ea5_1	spfh_2m1_1	spfh_2m2_1	spfh_2m3_1	spfh_2m4_1	spfh_2m5_1	tcdc_ea1_1	tcdc_ea2_1	tcdc_ea3_1	tcdc_ea4_1	tcdc_ea5_1	tcolc_e1_1	tcolc_e2_1	tcolc_e3_1	tcolc_e4_1	tcolc_e5_1	tmax_2m1_1	tmax_2m2_1	tmax_2m3_1	tmax_2m4_1	tmax_2m5_1	tmin_2m1_1	tmin_2m2_1	tmin_2m3_1	tmin_2m4_1	tmin_2m5_1	tmp_2m_1_1	tmp_2m_2_1	tmp_2m_3_1	tmp_2m_4_1	tmp_2m_5_1	tmp_sfc1_1	tmp_sfc2_1	tmp_sfc3_1	tmp_sfc4_1	tmp_sfc5_1	ulwrf_s1_1	ulwrf_s2_1	ulwrf_s3_1	ulwrf_s4_1	ulwrf_s5_1	ulwrf_t1_1	ulwrf_t2_1	ulwrf_t3_1	ulwrf_t4_1	ulwrf_t5_1	uswrf_s2_1	uswrf_s3_1	uswrf_s4_1	uswrf_s5_1	salida
V1	0.000000	0.0	268.583582	244.241641	251.174486	269.741308	268.377441	30.0	220.000000	510.000000	330.000000	101832.056108	102053.159091	102090.046165	101934.175426	101988.003551	5.879193	7.018182	8.460800	9.418182	9.727869	0.003229	0.002993	0.003775	0.003870	0.003855	0.000000	0.000000	0.000000	0.000000	0.000909	0.000818	0.000264	0.000255	0.000500	0.002218	280.789784	279.627444	285.727761	286.881681	286.885823	279.198020	278.472615	278.474720	285.799685	280.966961	279.249256	279.612202	285.742784	286.841053	280.960865	277.278370	279.250383	288.826760	288.596086	278.500078	341.122231	335.067918	354.626126	397.774053	383.281225	222.153166	252.504475	254.760271	263.342404	260.067843	10.000000	50.000000	106.636364	72.000000	11930700
V2	0.008182	0.2	251.725869	255.824126	272.163913	318.259924	307.929083	30.0	173.636364	333.636364	224.545455	101425.883523	101284.509233	101253.654830	100999.313920	101424.626420	12.534339	11.987316	12.159355	12.313590	13.469729	0.003737	0.003931	0.004015	0.003994	0.004826	0.037273	0.021818	0.101818	0.084545	0.109091	0.037155	0.021309	0.102373	0.085827	0.109336	278.822329	278.063379	283.618583	286.606684	286.643397	277.258919	276.740628	276.740628	283.687009	282.111078	277.282621	278.070390	283.604600	286.554729	282.105011	275.830009	278.269459	287.048970	287.325478	281.005252	330.159915	329.354673	347.524819	388.017767	378.773804	236.836691	233.458263	233.027276	212.652054	222.052916	8.181818	35.909091	58.181818	42.090909	9778500
V3	0.000000	0.0	219.734547	211.996022	216.405820	235.529123	239.840132	30.0	220.000000	523.636364	337.545455	102253.654119	102301.918324	102088.093750	101652.815341	101543.146307	5.726770	5.458528	5.700000	7.163636	9.536364	0.002003	0.001919	0.002107	0.002431	0.002583	0.000000	0.000000	0.007273	0.007273	0.042727	0.001427	0.001582	0.007309	0.006973	0.042127	275.400091	270.222512	275.885787	279.049513	279.381653	269.756037	269.157731	269.156439	276.041792	275.301960	269.766876	270.204285	275.880818	279.064603	275.806757	269.533059	271.690993	281.759993	282.686446	273.615503	309.639845	299.751961	317.250763	364.339136	351.496665	238.655654	232.828737	235.480750	245.177331	238.893102	10.272727	55.272727	118.454545	79.181818	9771900
V4	0.000000	0.0	253.499410	230.896544	235.857221	240.274556	237.804048	30.0	208.181818	512.727273	337.181818	102110.375710	102435.603693	102688.528409	102588.876420	102598.252841	7.889904	6.768959	6.208357	5.977267	6.411838	0.002918	0.002735	0.002771	0.002821	0.002738	0.000000	0.002727	0.005455	0.000909	0.012727	0.000473	0.004018	0.007300	0.001600	0.014882	279.396046	276.176919	276.868630	278.550368	278.572038	276.175482	273.839142	273.840535	276.942990	273.802970	276.312428	274.045715	276.877749	278.571555	273.812827	274.824765	274.466433	281.291418	281.871679	272.191753	330.310971	318.761563	329.305478	360.297788	348.618319	236.784869	241.916776	243.398572	251.473036	247.503769	8.909091	46.000000	107.090909	73.636364	6466800
V5	0.000000	0.0	234.890020	238.927051	246.850822	271.577246	275.572826	30.0	220.000000	517.272727	336.363636	101750.317472	101331.333807	100921.029119	100422.514915	100309.059659	10.783448	10.425542	10.362327	8.829511	9.647615	0.003274	0.003269	0.003066	0.003483	0.003788	0.000909	0.000909	0.000909	0.014545	0.050909	0.001673	0.001836	0.001373	0.015909	0.049591	273.294803	275.018022	283.542744	288.171156	288.265137	272.858415	273.303902	273.306355	283.734819	283.735446	273.314844	274.990234	283.563099	288.178922	285.567946	272.260426	275.132668	285.698725	288.490562	283.121391	310.023179	314.763264	334.042186	388.737835	383.409776	233.641681	233.706659	239.952805	258.128188	253.200684	8.909091	48.909091	106.272727	71.818182	11545200

	apcp_sf1_1	apcp_sf2_1	apcp_sf3_1	apcp_sf4_1	apcp_sf5_1	dlwrf_s1_1	dlwrf_s2_1	dlwrf_s3_1	dlwrf_s4_1	dlwrf_s5_1	dswrf_s1_1	dswrf_s2_1	dswrf_s3_1	dswrf_s4_1	dswrf_s5_1	pres_ms1_1	pres_ms2_1	pres_ms3_1	pres_ms4_1	pres_ms5_1	pwat_ea1_1	pwat_ea2_1	pwat_ea3_1	pwat_ea4_1	pwat_ea5_1	spfh_2m1_1	spfh_2m2_1	spfh_2m3_1	spfh_2m4_1	spfh_2m5_1	tcdc_ea1_1	tcdc_ea2_1	tcdc_ea3_1	tcdc_ea4_1	tcdc_ea5_1	tcolc_e1_1	tcolc_e2_1	tcolc_e3_1	tcolc_e4_1	tcolc_e5_1	tmax_2m1_1	tmax_2m2_1	tmax_2m3_1	tmax_2m4_1	tmax_2m5_1	tmin_2m1_1	tmin_2m2_1	tmin_2m3_1	tmin_2m4_1	tmin_2m5_1	tmp_2m_1_1	tmp_2m_2_1	tmp_2m_3_1	tmp_2m_4_1	tmp_2m_5_1	tmp_sfc1_1	tmp_sfc2_1	tmp_sfc3_1	tmp_sfc4_1	tmp_sfc5_1	ulwrf_s1_1	ulwrf_s2_1	ulwrf_s3_1	ulwrf_s4_1	ulwrf_s5_1	ulwrf_t1_1	ulwrf_t2_1	ulwrf_t3_1	ulwrf_t4_1	ulwrf_t5_1	uswrf_s1_1	uswrf_s2_1	uswrf_s3_1	uswrf_s4_1	uswrf_s5_1	salida
count	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4380.000000	4.380000e+03
mean	0.606809	0.252764	0.480446	0.271476	0.514641	316.698532	317.381851	324.682998	343.757513	343.097254	0.070693	163.547696	375.982939	685.232316	507.620569	101724.224223	101779.516825	101746.997257	101542.138914	101503.806442	21.412453	21.577143	22.172867	22.628942	22.419481	0.007851	0.008863	0.009370	0.009483	0.009927	0.069813	0.069181	0.065218	0.065575	0.061610	0.070115	0.069504	0.065527	0.065919	0.061997	286.998221	288.368218	292.889562	294.567249	294.625310	284.644851	284.697383	284.678400	292.825082	291.176236	284.897279	288.305418	292.829753	294.385505	291.386512	284.142094	289.314751	295.635538	295.995843	290.450591	376.183645	382.345516	401.169657	439.634087	431.819393	247.683051	247.300145	251.597699	261.916025	260.870143	0.076235	38.585222	76.064674	126.504047	99.087049	1.629826e+07
std	2.275740	0.999809	1.774720	1.086599	1.886735	55.637609	57.874376	58.702133	60.860672	60.693451	0.295001	112.394328	159.517593	228.049707	193.860461	721.839730	728.790723	718.552527	698.496042	714.652866	12.136053	12.264956	12.495203	12.543698	12.310466	0.004374	0.005020	0.005165	0.005088	0.005442	0.167006	0.170252	0.169748	0.169143	0.162010	0.166901	0.170133	0.169632	0.169005	0.161853	8.851120	9.686553	9.827525	9.708510	9.696085	8.663294	8.796469	8.800662	9.880096	10.026533	8.652794	9.738082	9.873836	9.716331	10.015781	8.794389	9.702728	9.070324	9.231690	10.402144	46.178756	49.619215	50.445754	52.695819	53.978436	36.353242	36.804219	36.382653	39.190955	38.734023	0.255491	25.984861	30.784078	40.571154	35.454368	7.965298e+06
min	0.000000	0.000000	0.000000	0.000000	0.000000	158.971770	160.032903	165.524543	183.671312	186.342961	0.000000	0.000000	20.000000	30.000000	20.000000	99316.970881	99097.036222	98946.524858	99010.708097	98830.153409	1.100000	1.314819	1.107352	1.142803	1.201246	0.000462	0.000485	0.000451	0.000478	0.000468	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	254.589220	254.937418	258.549777	260.800365	260.863475	251.941358	249.576132	249.576714	258.698331	258.171345	251.942065	254.844406	258.552646	260.795430	258.170049	250.100794	256.360800	263.634377	264.533564	256.520408	227.242239	223.985486	246.314349	278.576630	271.707606	104.671267	113.559602	118.679132	119.393449	121.951425	0.000000	0.000000	3.181818	4.363636	2.545455	5.100000e+05
25%	0.000000	0.000000	0.000000	0.000000	0.000000	271.219564	268.889864	275.709318	293.757163	292.834810	0.000000	52.727273	240.000000	524.545455	343.977273	101266.292436	101311.457564	101283.620206	101100.136186	101050.542436	10.971268	10.849190	11.235352	11.630796	11.657611	0.003998	0.004264	0.004628	0.004745	0.004817	0.000000	0.000000	0.000000	0.000000	0.000000	0.000627	0.000573	0.000545	0.000691	0.000736	280.341776	280.616793	285.221118	287.201786	287.274496	278.056773	277.877620	277.874818	285.055183	283.568461	278.246751	280.472266	285.052367	287.063607	283.701843	277.188510	281.579497	288.821357	289.178255	282.239473	339.283260	341.152538	359.526828	399.347294	389.547691	230.499519	230.482578	233.976283	245.721419	243.850282	0.000000	14.000000	53.431818	108.454545	74.795455	1.062375e+07
50%	0.000000	0.000000	0.000000	0.000000	0.000000	321.106932	323.063311	329.708327	346.763388	345.883918	0.000000	149.090909	382.000000	725.454545	524.954545	101655.984730	101712.774503	101680.867188	101479.565696	101430.512784	19.312292	19.302599	19.763636	20.431517	20.389514	0.007257	0.008292	0.008952	0.009197	0.009563	0.005455	0.004545	0.003636	0.003636	0.003636	0.005150	0.004959	0.004136	0.004514	0.003759	287.690272	289.161910	293.823814	295.576867	295.664069	285.190432	285.319819	285.210710	293.664572	291.892625	285.524544	289.075811	293.796660	295.402143	292.148650	284.734411	290.105994	296.272095	296.772838	291.155626	376.667526	383.600055	401.964017	441.313997	434.092922	253.198004	253.309217	257.270334	270.790095	269.300432	0.000000	35.363636	79.272727	135.909091	104.818182	1.635690e+07
75%	0.109091	0.051818	0.127500	0.036364	0.090909	366.145476	370.147353	378.509126	399.600853	398.728281	0.000000	262.727273	522.545455	892.045455	691.750000	102141.220348	102200.644176	102152.131214	101943.061435	101919.519709	30.959091	31.400947	32.354219	32.975179	32.373798	0.011580	0.013512	0.014259	0.014150	0.015061	0.056364	0.059091	0.044545	0.045455	0.039091	0.056350	0.059170	0.044798	0.044630	0.039120	294.395465	296.994538	301.447272	302.796303	302.828929	292.226128	292.565418	292.550144	301.434116	299.956680	292.520470	296.991716	301.433807	302.711204	300.108120	292.049802	297.934257	303.308531	303.679695	299.685545	416.400996	428.011458	445.903448	483.390056	477.311875	274.555647	274.861445	278.800420	290.139446	289.699652	0.000000	61.818182	102.727273	155.204545	129.545455	2.312775e+07
max	43.009090	16.846364	28.399091	26.381818	36.875455	426.173970	427.486894	429.693146	455.566337	453.910406	3.000000	381.818182	643.545455	990.000000	791.090909	104688.396307	104856.285511	104693.185369	104244.932528	104249.968040	60.327273	58.876881	59.915362	59.309182	60.529133	0.018809	0.019533	0.020985	0.021932	0.023318	1.920909	2.370000	2.449091	2.146364	1.957273	1.920136	2.369282	2.450482	2.146409	1.956655	304.480122	304.792880	311.277519	312.660564	312.668726	300.350930	299.724509	299.735546	310.815957	308.761763	300.344230	304.773410	311.272270	312.595520	309.005149	299.869093	306.834309	315.964081	313.965757	308.770635	470.753102	469.429213	504.584351	555.704024	542.529280	318.245345	311.991660	315.569164	328.920274	327.253141	1.000000	92.272727	192.636364	450.636364	313.909091	3.122700e+07

Práctica 1. Aprendizaje Automático¶

0. Table of contents¶

1. Requirements¶

1.1. Cleaning ../data/img/ folder¶

2. Reading the datasets¶

3. EDA¶

3.1. Dataset description¶

3.2. Missing values¶

Observations¶

Conclusions¶

3.3. Outliers¶

3.4. Correlation¶

Getting the correlation matrix formatted into our own data structure¶

Correlation Heat Map¶

4. Train-Test division¶

TODO: PREGUNTAR SI SE PUEDE USAR ILOC EN VEZ DE TRAIN TEST SPLIT¶

X. Output the Jupyter Notebook as an HTML file¶